From aeca88ba6d25227912f508591cb33784d2a232f5 Mon Sep 17 00:00:00 2001
From: Alina Voilova <alina.voilova@lichtblick.de>
Date: Tue, 6 Feb 2024 13:08:38 +0100
Subject: [PATCH] added slice property & tests

---
 docs/core/pfline.rst                    |    7 +-
 docs/tutorial/part3.ipynb               | 1910 ++++++++++++-----------
 docs/tutorial/part4.ipynb               |  556 +++----
 portfolyo/core/ndframelike.py           |    6 +
 portfolyo/core/pfline/classes.py        |    2 +
 portfolyo/core/pfline/flat_methods.py   |   25 +
 portfolyo/core/pfline/nested_methods.py |   17 +
 portfolyo/core/pfstate/pfstate.py       |   18 +
 tests/core/pfline/test_slice.py         |   91 ++
 tests/core/pfstate/test_slice_state.py  |   68 +
 10 files changed, 1475 insertions(+), 1225 deletions(-)
 create mode 100644 tests/core/pfline/test_slice.py
 create mode 100644 tests/core/pfstate/test_slice_state.py

diff --git a/docs/core/pfline.rst b/docs/core/pfline.rst
index e3a8678..5a6756d 100644
--- a/docs/core/pfline.rst
+++ b/docs/core/pfline.rst
@@ -252,6 +252,8 @@ Index slice
 
 From ``pandas`` we know the ``.loc[]`` property which allows us to select a slice of the objects. This is implemented also for portfolio lines. Currently, it supports enering a slice of timestamps. It is a wrapper around the ``pandas.DataFrame.loc[]`` property, and therefore follows the same convention, with the end point being included in the result.
 
+Another slicing method is implemented with the ``.slice[]`` property. The improvement to ``.loc[]`` is, that ``.slice[]`` uses the more common convention of excluding the end point. This has several advantages, which stem from the fact that, unlike when using ``.loc``, using ``left = pfl.slice[:a]`` and ``right = pfl.slice[a:]`` returns portfolio lines that are complements - every timestamp in the original portfolio line is found in either the left or the right slice. This is useful when e.g. concatenating portfolio lines (see below.)
+
 .. exec_code::
 
    # --- hide: start ---
@@ -261,12 +263,13 @@ From ``pandas`` we know the ``.loc[]`` property which allows us to select a slic
    pfl = pf.PfLine(input_df)
    # --- hide: stop ---
    # continuation of previous code example
-   pfl.loc['2024':'2025']  # includes 2025
+   pfl.slice['2024':'2026']  # excludes 2026; 2026 interpreted as timestamp 2026-01-01 00:00:00
    # --- hide: start ---
-   print(pfl.loc['2024':'2025'])
+   print(pfl.slice['2024':'2026'])
    # --- hide: stop ---
 
 
+
 Volume-only, price-only or revenue-only
 =======================================
 
diff --git a/docs/tutorial/part3.ipynb b/docs/tutorial/part3.ipynb
index e71e8bf..1abbd36 100644
--- a/docs/tutorial/part3.ipynb
+++ b/docs/tutorial/part3.ipynb
@@ -1,948 +1,968 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Tutorial part 3\n",
-                "\n",
-                "In [part 1](part1.ipynb) and [part 2](part2.ipynb) we have learnt about portfolio lines. These are timeseries, or collections of timeseries, describing the volumes and/or prices during various delivery periods.\n",
-                "\n",
-                "In this part, we'll combine portfolio lines into a \"portfolio state\" (``PfState``) object. As we'll see, some of the methods and properties we know from the ``PfLine`` class also apply here.\n",
-                "\n",
-                "\n",
-                "## Example data\n",
-                "\n",
-                "Let's again use the mock functions to get some portfolio lines. (The parameter details here are not important, we just want some more-or-less realistic data). To change things up a bit from the previous tutorial parts, we'll look at about 80 days in the autumn of 2024, in quarterhourly (``\"15T\"``) resolution. And let's localize the data to a specific timezone:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import portfolyo as pf\n",
-                "import pandas as pd\n",
-                "\n",
-                "index = pd.date_range('2024-09-20', '2024-12-10', freq='15T', inclusive='left', tz='Europe/Berlin')\n",
-                "# Creating offtake portfolio line.\n",
-                "ts_offtake = -1 * pf.dev.w_offtake(index, avg=50)\n",
-                "offtake = pf.PfLine({'w': ts_offtake})\n",
-                "# Creating portfolio line with market prices (here: price-forward curve).\n",
-                "ts_prices = pf.dev.p_marketprices(index, avg=200)\n",
-                "prices = pf.PfLine({'p': ts_prices})\n",
-                "\n",
-                "# Creating portfolio line with sourced volume.\n",
-                "ts_sourced_power1, ts_sourced_price1 = pf.dev.wp_sourced(ts_offtake, 'QS', 0.3, p_avg=120)\n",
-                "sourced_quarters = pf.PfLine({'w': ts_sourced_power1, 'p': ts_sourced_price1})\n",
-                "ts_sourced_power2, ts_sourced_price2 = pf.dev.wp_sourced(ts_offtake, 'MS', 0.2, p_avg=150)\n",
-                "sourced_months = pf.PfLine({'w': ts_sourced_power2, 'p': ts_sourced_price2})\n",
-                "sourced = pf.PfLine({'quarter_products': sourced_quarters, 'month_products': sourced_months})"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now use these portfolio lines to create a portfolio state.\n",
-                "\n",
-                "## Portfolio State\n",
-                "\n",
-                "The ``PfState`` class is used to hold information about offtake, market prices, and sourcing. Let's create one from the portfolio lines we just created:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "PfState object.\n",
-                            ". Start: 2024-09-20 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                            ". End  : 2024-12-10 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                            ". Freq : <15 * Minutes> (7780 datapoints)\n",
-                            "                                                w           q           p             r\n",
-                            "                                               MW         MWh     Eur/MWh           Eur\n",
-                            "──────── offtake\n",
-                            "           2024-09-20 00:00:00 +0200        -45.8         -11                          \n",
-                            "           2024-09-20 00:15:00 +0200        -44.4         -11                          \n",
-                            "           ..                                  ..          ..          ..            ..\n",
-                            "           2024-12-09 23:30:00 +0100        -60.5         -15                          \n",
-                            "           2024-12-09 23:45:00 +0100        -58.3         -15                          \n",
-                            "─●────── pnl_cost\n",
-                            " │         2024-09-20 00:00:00 +0200         45.8          11      136.54         1 564\n",
-                            " │         2024-09-20 00:15:00 +0200         44.4          11      135.21         1 500\n",
-                            " │         ..                                  ..          ..          ..            ..\n",
-                            " │         2024-12-09 23:30:00 +0100         60.5          15      167.89         2 540\n",
-                            " │         2024-12-09 23:45:00 +0100         58.3          15      167.21         2 436\n",
-                            " ├●───── sourced\n",
-                            " ││        2024-09-20 00:00:00 +0200         31.3           8      135.03         1 058\n",
-                            " ││        2024-09-20 00:15:00 +0200         31.3           8      135.03         1 058\n",
-                            " ││        ..                                  ..          ..          ..            ..\n",
-                            " ││        2024-12-09 23:30:00 +0100         35.7           9      126.09         1 124\n",
-                            " ││        2024-12-09 23:45:00 +0100         35.7           9      126.09         1 124\n",
-                            " │├───── quarter_products\n",
-                            " ││        2024-09-20 00:00:00 +0200         16.4           4      102.48           421\n",
-                            " ││        2024-09-20 00:15:00 +0200         16.4           4      102.48           421\n",
-                            " ││        ..                                  ..          ..          ..            ..\n",
-                            " ││        2024-12-09 23:30:00 +0100         13.4           3      111.14           372\n",
-                            " ││        2024-12-09 23:45:00 +0100         13.4           3      111.14           372\n",
-                            " │└───── month_products\n",
-                            " │         2024-09-20 00:00:00 +0200         14.9           4      170.85           637\n",
-                            " │         2024-09-20 00:15:00 +0200         14.9           4      170.85           637\n",
-                            " │         ..                                  ..          ..          ..            ..\n",
-                            " │         2024-12-09 23:30:00 +0100         22.3           6      135.07           752\n",
-                            " │         2024-12-09 23:45:00 +0100         22.3           6      135.07           752\n",
-                            " └────── unsourced\n",
-                            "           2024-09-20 00:00:00 +0200         14.5           4      139.81           506\n",
-                            "           2024-09-20 00:15:00 +0200         13.0           3      135.64           442\n",
-                            "           ..                                  ..          ..          ..            ..\n",
-                            "           2024-12-09 23:30:00 +0100         24.8           6      227.88         1 416\n",
-                            "           2024-12-09 23:45:00 +0100         22.6           6      232.05         1 312"
-                        ]
-                    },
-                    "execution_count": 2,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pfs = pf.PfState(offtake, prices, sourced)\n",
-                "\n",
-                "pfs"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Note from how these portfolio lines were created, that ``offtake`` has negative values. The sign conventions are discussed [here](../core/pfstate.rst#sign-conventions).\n",
-                "\n",
-                "This portfolio state contains values for every quarterhour in the specified time period. Let's see what features this class has, starting with two methods we already met when discussing the ``PfLine`` class.\n",
-                "\n",
-                "### Plotting\n",
-                "\n",
-                "Just as when working with portfolio lines, we can get a quick overview of the portfolio state with the ``.plot()`` method..."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 720x432 with 4 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "pfs.plot();"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "...and we can copy its data to the clipboard, or save it as an Excel workbook:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "pfs.to_clipboard()\n",
-                "pfs.to_excel('portfolio_state.xlsx')"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Looking at the offtake, we can see the daily and weekly cycles, as well as a slow increase over the entire time period.\n",
-                "\n",
-                "This graph is a bit too detailed for most purposes, so let's look at the second method we know from ``PfLine``: resampling.\n",
-                "\n",
-                "### Resampling\n",
-                "\n",
-                "We might prefer to see hourly, daily, or monthly values instead of the quarterhourly values that are in ``pfs``. For this, we can resample the object with the ``.asfreq()`` method. In this code example, we also use the ``.print()`` method, which adds some helpful coloring to the output:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "PfState object.\n",
-                        ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                        ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                        ". Freq : <MonthBegin> (2 datapoints)\n",
-                        "                                                w           q           p             r\n",
-                        "                                               MW         MWh     Eur/MWh           Eur\n",
-                        "\u001b[1m\u001b[37m──────── offtake\n",
-                        "  \u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200        -54.7     -40 744                          \n",
-                        "  \u001b[1m\u001b[37m         \u001b[0m2024-11-01 00:00:00 +0100        -59.4     -42 732                          \n",
-                        "\u001b[1m\u001b[37m─\u001b[1m\u001b[33m●\u001b[1m\u001b[37m────── pnl_cost\n",
-                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
-                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452\n",
-                        " \u001b[1m\u001b[33m├\u001b[1m\u001b[36m●\u001b[1m\u001b[33m───── sourced\n",
-                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         31.2      23 221      132.58     3 078 642\n",
-                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-11-01 00:00:00 +0100         25.9      18 652      133.27     2 485 849\n",
-                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m├───── quarter_products\n",
-                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         13.8      10 256      106.54     1 092 646\n",
-                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-11-01 00:00:00 +0100         13.7       9 897      106.78     1 056 810\n",
-                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m└───── month_products\n",
-                        " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         17.4      12 964      153.19     1 985 996\n",
-                        " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-11-01 00:00:00 +0100         12.2       8 755      163.22     1 429 039\n",
-                        " \u001b[1m\u001b[33m└────── unsourced\n",
-                        "   \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         23.5      17 523      197.44     3 459 829\n",
-                        "   \u001b[1m\u001b[33m        \u001b[0m2024-11-01 00:00:00 +0100         33.4      24 080      221.41     5 331 603\n"
-                    ]
-                }
-            ],
-            "source": [
-                "pfs_monthly = pfs.asfreq('MS')\n",
-                "pfs_monthly.print()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Note how the resampled output only contains those months, that are *entirely* included in the original data. ``pfl`` has data in September and December, but as these are not present in their entirety, they are dropped from ``pfl_monthly``.\n",
-                "\n",
-                "### On frequencies and unsourced prices\n",
-                "\n",
-                "There is one other important consequence of resampling: the unsourced prices are now *specific for this portfolio*. We can demonstrate this by creating a second portfolio state, using the *same price-forward curve* (but different offtake and sourced volume):"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "pfs2 = pf.PfState(offtake*1.5, prices, sourced*2)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "At this shortest frequency, the unsourced prices are identical (namely, the price-forward curve). Let's create a dataframe with the prices to verify this:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>pfs</th>\n",
-                            "      <th>pfs2</th>\n",
-                            "      <th>hpfc</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>2024-09-20 00:00:00+02:00</th>\n",
-                            "      <td>139.80568207211454</td>\n",
-                            "      <td>139.80568207211454</td>\n",
-                            "      <td>139.80568207211454</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-09-20 00:15:00+02:00</th>\n",
-                            "      <td>135.63901540544785</td>\n",
-                            "      <td>135.63901540544785</td>\n",
-                            "      <td>135.63901540544785</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-09-20 00:30:00+02:00</th>\n",
-                            "      <td>131.47234873878116</td>\n",
-                            "      <td>131.47234873878116</td>\n",
-                            "      <td>131.47234873878116</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-09-20 00:45:00+02:00</th>\n",
-                            "      <td>128.13901540544785</td>\n",
-                            "      <td>128.13901540544785</td>\n",
-                            "      <td>128.13901540544785</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-09-20 01:00:00+02:00</th>\n",
-                            "      <td>124.80568207211451</td>\n",
-                            "      <td>124.80568207211451</td>\n",
-                            "      <td>124.80568207211451</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>...</th>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-12-09 22:45:00+01:00</th>\n",
-                            "      <td>217.88078474854294</td>\n",
-                            "      <td>217.88078474854294</td>\n",
-                            "      <td>217.88078474854294</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-12-09 23:00:00+01:00</th>\n",
-                            "      <td>221.2141180818763</td>\n",
-                            "      <td>221.2141180818763</td>\n",
-                            "      <td>221.2141180818763</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-12-09 23:15:00+01:00</th>\n",
-                            "      <td>224.54745141520962</td>\n",
-                            "      <td>224.54745141520962</td>\n",
-                            "      <td>224.54745141520962</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-12-09 23:30:00+01:00</th>\n",
-                            "      <td>227.88078474854294</td>\n",
-                            "      <td>227.88078474854294</td>\n",
-                            "      <td>227.88078474854294</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-12-09 23:45:00+01:00</th>\n",
-                            "      <td>232.0474514152096</td>\n",
-                            "      <td>232.0474514152096</td>\n",
-                            "      <td>232.0474514152096</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>7780 rows × 3 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                          pfs                pfs2  \\\n",
-                            "2024-09-20 00:00:00+02:00  139.80568207211454  139.80568207211454   \n",
-                            "2024-09-20 00:15:00+02:00  135.63901540544785  135.63901540544785   \n",
-                            "2024-09-20 00:30:00+02:00  131.47234873878116  131.47234873878116   \n",
-                            "2024-09-20 00:45:00+02:00  128.13901540544785  128.13901540544785   \n",
-                            "2024-09-20 01:00:00+02:00  124.80568207211451  124.80568207211451   \n",
-                            "...                                       ...                 ...   \n",
-                            "2024-12-09 22:45:00+01:00  217.88078474854294  217.88078474854294   \n",
-                            "2024-12-09 23:00:00+01:00   221.2141180818763   221.2141180818763   \n",
-                            "2024-12-09 23:15:00+01:00  224.54745141520962  224.54745141520962   \n",
-                            "2024-12-09 23:30:00+01:00  227.88078474854294  227.88078474854294   \n",
-                            "2024-12-09 23:45:00+01:00   232.0474514152096   232.0474514152096   \n",
-                            "\n",
-                            "                                         hpfc  \n",
-                            "2024-09-20 00:00:00+02:00  139.80568207211454  \n",
-                            "2024-09-20 00:15:00+02:00  135.63901540544785  \n",
-                            "2024-09-20 00:30:00+02:00  131.47234873878116  \n",
-                            "2024-09-20 00:45:00+02:00  128.13901540544785  \n",
-                            "2024-09-20 01:00:00+02:00  124.80568207211451  \n",
-                            "...                                       ...  \n",
-                            "2024-12-09 22:45:00+01:00  217.88078474854294  \n",
-                            "2024-12-09 23:00:00+01:00   221.2141180818763  \n",
-                            "2024-12-09 23:15:00+01:00  224.54745141520962  \n",
-                            "2024-12-09 23:30:00+01:00  227.88078474854294  \n",
-                            "2024-12-09 23:45:00+01:00   232.0474514152096  \n",
-                            "\n",
-                            "[7780 rows x 3 columns]"
-                        ]
-                    },
-                    "execution_count": 7,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pd.DataFrame({'pfs': pfs.unsourcedprice.p, 'pfs2': pfs2.unsourcedprice.p, 'hpfc': prices.p})"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "However, at every other frequency, they are not equal. When changing the frequency, a *volume-weighted* average is calculated for the unsourced prices - just like with every other price-and-volume timeseries. This makes that the unsourced prices apply only to the unsourced volume profile *of that portfolio*:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>pfs</th>\n",
-                            "      <th>pfs2</th>\n",
-                            "      <th>hpfc</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>2024-10-01 00:00:00+02:00</th>\n",
-                            "      <td>197.4437803760265</td>\n",
-                            "      <td>192.11463549822653</td>\n",
-                            "      <td>194.78888729464586</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-11-01 00:00:00+01:00</th>\n",
-                            "      <td>221.40989836363784</td>\n",
-                            "      <td>217.79829542112196</td>\n",
-                            "      <td>220.07479863376392</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                          pfs                pfs2  \\\n",
-                            "2024-10-01 00:00:00+02:00   197.4437803760265  192.11463549822653   \n",
-                            "2024-11-01 00:00:00+01:00  221.40989836363784  217.79829542112196   \n",
-                            "\n",
-                            "                                         hpfc  \n",
-                            "2024-10-01 00:00:00+02:00  194.78888729464586  \n",
-                            "2024-11-01 00:00:00+01:00  220.07479863376392  "
-                        ]
-                    },
-                    "execution_count": 8,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pd.DataFrame({'pfs': pfs.asfreq('MS').unsourcedprice.p, 'pfs2': pfs2.asfreq('MS').unsourcedprice.p, 'hpfc': prices.asfreq('MS').p})"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "This has important consequences. \n",
-                "\n",
-                "For example, when doing a scenario analysis in which the unsourced volume is changed (e.g. \"what happens if the offtake increases by 50%?\"), we cannot expect the results to be correct *unless we are working at the original frequency*. In situations where it is clear that this error looms, a ``UserWarning`` is shown to alert the user (e.g. in the examples further below). For more information on unsourced volume, [see this section](../core/pfstate.rst#Unsourced-price) in the documentation on the `PfState` class.\n",
-                "\n",
-                "For this reason, we commonly work with our porfolio states at the frequency of the price-forward curve. Downsampling is only done to see the aggregated values for verification or reporting."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Components\n",
-                "\n",
-                "Now, let's look at the portfolio state ``pfs_monthly`` in a bit more detail, to learn more about the ``PfState`` class.\n",
-                "\n",
-                "The portfolio state is presented to us as a tree structure, with several branches. Each branch is a portfolio line. E.g, ``offtake`` and ``sourced`` are the portfolio lines we specified when creating the object. Also, the branch ``pnl_cost`` is the sum of ``sourced`` and ``unsourced``, with ``sourced`` being the sum of ``quarter_products`` and ``month_products``. \n",
-                "\n",
-                "The unsourced volume is found by comparing the offtake to what is already sourced. This volume is valued at the market prices in the forward curve.\n",
-                "\n",
-                "These portfolio lines can be obtained from the portfolio state by accessing them as attributes. E.g. ``.offtakevolume``, ``.sourced``, ``.unsourced``, or ``.pnl_cost``. The latter is the best estimate for what it will cost to procure the offtake:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "PfLine object with price and volume information.\n",
-                            ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                            ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                            ". Freq : <MonthBegin> (2 datapoints)\n",
-                            ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
-                            "                                     w           q           p             r\n",
-                            "                                    MW         MWh     Eur/MWh           Eur\n",
-                            "\n",
-                            "2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
-                            "2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452"
-                        ]
-                    },
-                    "execution_count": 9,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pfs_monthly.pnl_cost"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Notice that this portfolio line has children, and as a reminder, we can \"drill into\" the object to get these nested portfolio line, e.g. with ``pfl_monthly.pnl_cost[\"sourced\"]``.\n",
-                "\n",
-                "There are some other components that are not explicitly shown:\n",
-                "\n",
-                "* We may be interested in how much of the offtake has already been sourced or unsourced. These fractions are available at the ``.sourcedfraction`` and ``.unsourcedfraction`` properties.\n",
-                "\n",
-                "* You may have noticed that ``unsourced`` is the inverse from what traders would call the \"open positions\" or \"portfolio positions\": if our portfolio is short, the unsourced volume is positive. For those that prefer this other perspective, it is available at ``.netposition``.\n",
-                "\n",
-                "### Export\n",
-                "\n",
-                "Just as with portfolio lines, we can create an excel file that contains all the information in a portfolio state with its ``.to_excel()`` method, and we can copy it to the clipboard with the ``.to_clipboard()`` method.\n",
-                "\n",
-                "### MtM\n",
-                "\n",
-                "We can evaluate the value of our sourcing contracts against the current forward curve (\"mark-to-market\") with the ``.mtm_of_sourced()`` method."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Analyses with portfolio states\n",
-                "\n",
-                "We'll now look at how we can do \"what-if\" analyses with portfolio state. The original portfolio state we will consider as the reference and store it in an appropriately named variable:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "ref = pfs"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The monthly procurement prices of this portfolio are what interest us the most. As a reminder, we can find the procurement volumes and costs with:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "PfLine object with price and volume information.\n",
-                            ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                            ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                            ". Freq : <MonthBegin> (2 datapoints)\n",
-                            ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
-                            "                                     w           q           p             r\n",
-                            "                                    MW         MWh     Eur/MWh           Eur\n",
-                            "\n",
-                            "2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
-                            "2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452"
-                        ]
-                    },
-                    "execution_count": 11,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "cost_ref = ref.asfreq('MS').pnl_cost\n",
-                "cost_ref"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "(Or we could go one step further and focus on only the prices with ``ref.asfreq(\"MS\").pnl_cost.p``.)\n",
-                "\n",
-                "### Change in offtake\n",
-                "\n",
-                "Now, what would happen if the offtake were to increase by 25%? Qualitatively, this is not hard. An increase in the offtake increases the unsourced volume. And because the market prices are higher than what we pay for the sourced volume, this means that the procurement price will go up. \n",
-                "\n",
-                "How much? Let's see. First, we create a new portfolio state, from the reference, by setting the offtake to the new value. We can do this with the ``.set_offtake()`` method. After that, we can again see what the procurement volumes and costs are. (Note the ``UserWarning`` which was mentioned [above](#on-frequencies-and-unsourced-pricesE).)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:199: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
-                        "  warnings.warn(\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "PfLine object with price and volume information.\n",
-                            ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                            ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                            ". Freq : <MonthBegin> (2 datapoints)\n",
-                            ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
-                            "                                     w           q           p             r\n",
-                            "                                    MW         MWh     Eur/MWh           Eur\n",
-                            "\n",
-                            "2024-10-01 00:00:00 +0200         68.4      50 930      168.64     8 588 719\n",
-                            "2024-11-01 00:00:00 +0100         74.2      53 416      191.54    10 231 182"
-                        ]
-                    },
-                    "execution_count": 12,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "higherofftake = ref.offtakevolume * 1.25\n",
-                "pfs_higherofftake = ref.set_offtakevolume(higherofftake)\n",
-                "cost_higherofftake = pfs_higherofftake.asfreq(\"MS\").pnl_cost\n",
-                "cost_higherofftake"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Comparing these two ``cost`` portfolio lines, we see that indeed the values for ``w`` and ``q`` have increased to 125% of the original values. Also, the procurement prices have increased. We can quickly calculate by how much:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "2024-10-01 00:00:00+02:00    8.160879013401626\n",
-                            "2024-11-01 00:00:00+01:00    8.599884206454362\n",
-                            "Freq: MS, Name: p, dtype: pint[Eur/MWh]"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "cost_higherofftake.p - cost_ref.p"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We could similarly create a portfolio states for situations with a market price drop of 40%. Or one which combines both effects:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:199: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
-                        "  warnings.warn(\n"
-                    ]
-                }
-            ],
-            "source": [
-                "lowerprices = ref.unsourcedprice * 0.6\n",
-                "pfs_lowerprices = ref.set_unsourcedprice(lowerprices)\n",
-                "pfs_lowerprices_higherofftake = ref.set_offtakevolume(higherofftake).set_unsourcedprice(lowerprices)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Hedging\n",
-                "\n",
-                "Hedging can reduce the sensitivity of our portfolio to changes in the market price. Given the current market price curve, we can calculate how much we'd need to source to obtain a fully hedged portfolio:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "needed = ref.hedge_of_unsourced(\"val\", \"MS\")  # value hedge with month products"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Let's say we procure exactly that volume. We can add it to the sourced volume in our portfolio state:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:209: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
-                        "  warnings.warn(\n"
-                    ]
-                }
-            ],
-            "source": [
-                "hedged = ref.add_sourced(pf.PfLine({\"newvolume\": needed}))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "(We could have obtained the same result with the ``ref.source_unsourced()`` method.)\n",
-                "\n",
-                "The portfolio is now hedged at the month level. We can verify this by looking at the unsourced volume. In case of a volume hedge, the unsourced volume (``q`` and ``w``) is 0, even if its monetary value (``r``) is not; in case of a value hedge, it is the reverse:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "PfLine object with price and volume information.\n",
-                            ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                            ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                            ". Freq : <MonthBegin> (2 datapoints)\n",
-                            "                                     w           q           p             r\n",
-                            "                                    MW         MWh     Eur/MWh           Eur\n",
-                            "\n",
-                            "2024-10-01 00:00:00 +0200         -0.2        -173        0.00            -0\n",
-                            "2024-11-01 00:00:00 +0100         -0.2        -134        0.00            -0"
-                        ]
-                    },
-                    "execution_count": 17,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "hedged.asfreq('MS').unsourced"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Because the market prices have not changed, the best-estimate procurement prices (at month level and longer) are also unchanged from before. (This is verified in the \"before\" columns of the dataframe further below.)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Market price change\n",
-                "\n",
-                "A hedged profile is less impacted by market price changes. To see that this is indeed the case, let's look at a scenario with an increase in the forward price curve by 40 Eur/MWh, for both portfolio states:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "newprices = prices + pf.Q_(40.0, 'Eur/MWh')\n",
-                "ref_higherprices = ref.set_unsourcedprice(newprices)\n",
-                "hedged_higherprices = hedged.set_unsourcedprice(newprices)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The reference portfolio has gotten a lot more expensive, whereas the procurement price for the hedged portfolio has not moved significantly:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead tr th {\n",
-                            "        text-align: left;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th colspan=\"2\" halign=\"left\">ref</th>\n",
-                            "      <th colspan=\"2\" halign=\"left\">hedged</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th>before</th>\n",
-                            "      <th>after</th>\n",
-                            "      <th>before</th>\n",
-                            "      <th>after</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>unit</th>\n",
-                            "      <th>Eur/MWh</th>\n",
-                            "      <th>Eur/MWh</th>\n",
-                            "      <th>Eur/MWh</th>\n",
-                            "      <th>Eur/MWh</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>2024-10-01 00:00:00+02:00</th>\n",
-                            "      <td>160.478106</td>\n",
-                            "      <td>177.681366</td>\n",
-                            "      <td>160.478106</td>\n",
-                            "      <td>160.307936</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2024-11-01 00:00:00+01:00</th>\n",
-                            "      <td>182.939602</td>\n",
-                            "      <td>205.480086</td>\n",
-                            "      <td>182.939602</td>\n",
-                            "      <td>182.814260</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                  ref                  hedged            \n",
-                            "                               before       after      before       after\n",
-                            "unit                          Eur/MWh     Eur/MWh     Eur/MWh     Eur/MWh\n",
-                            "2024-10-01 00:00:00+02:00  160.478106  177.681366  160.478106  160.307936\n",
-                            "2024-11-01 00:00:00+01:00  182.939602  205.480086  182.939602  182.814260"
-                        ]
-                    },
-                    "execution_count": 19,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pd.DataFrame({\n",
-                "    ('ref', 'before'): ref.pnl_cost.asfreq('MS').p, \n",
-                "    ('ref', 'after'): ref_higherprices.pnl_cost.asfreq('MS').p, \n",
-                "    ('hedged', 'before'): hedged.pnl_cost.asfreq('MS').p, \n",
-                "    ('hedged', 'after'): hedged_higherprices.pnl_cost.asfreq('MS').p,\n",
-                "}).pint.dequantify()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "For the observant reader: it may seem that the portfolio was not fully hedged after all, as a small change in the procurement price is seen. The reason is that each strategy (i.e., volume or value hedge) fully protects only against a specific price change (i.e., absolute or relative). A volume hedge does not *fully* hedge against an absolute price change such as the one we see here."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "This tutorial is continued [in part 4](part4.ipynb)."
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3.8.13 ('pf38')",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.13"
-        },
-        "orig_nbformat": 4,
-        "vscode": {
-            "interpreter": {
-                "hash": "642a4be8010ca5d45039b988c1d8379a91572488c4d23a0b88e966c6713c7e45"
-            }
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial part 3\n",
+    "\n",
+    "In [part 1](part1.ipynb) and [part 2](part2.ipynb) we have learnt about portfolio lines. These are timeseries, or collections of timeseries, describing the volumes and/or prices during various delivery periods.\n",
+    "\n",
+    "In this part, we'll combine portfolio lines into a \"portfolio state\" (``PfState``) object. As we'll see, some of the methods and properties we know from the ``PfLine`` class also apply here.\n",
+    "\n",
+    "\n",
+    "## Example data\n",
+    "\n",
+    "Let's again use the mock functions to get some portfolio lines. (The parameter details here are not important, we just want some more-or-less realistic data). To change things up a bit from the previous tutorial parts, we'll look at about 80 days in the autumn of 2024, in quarterhourly (``\"15T\"``) resolution. And let's localize the data to a specific timezone:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import portfolyo as pf\n",
+    "import pandas as pd\n",
+    "\n",
+    "index = pd.date_range(\n",
+    "    \"2024-09-20\", \"2024-12-10\", freq=\"15T\", inclusive=\"left\", tz=\"Europe/Berlin\"\n",
+    ")\n",
+    "# Creating offtake portfolio line.\n",
+    "ts_offtake = -1 * pf.dev.w_offtake(index, avg=50)\n",
+    "offtake = pf.PfLine({\"w\": ts_offtake})\n",
+    "# Creating portfolio line with market prices (here: price-forward curve).\n",
+    "ts_prices = pf.dev.p_marketprices(index, avg=200)\n",
+    "prices = pf.PfLine({\"p\": ts_prices})\n",
+    "\n",
+    "# Creating portfolio line with sourced volume.\n",
+    "ts_sourced_power1, ts_sourced_price1 = pf.dev.wp_sourced(\n",
+    "    ts_offtake, \"QS\", 0.3, p_avg=120\n",
+    ")\n",
+    "sourced_quarters = pf.PfLine({\"w\": ts_sourced_power1, \"p\": ts_sourced_price1})\n",
+    "ts_sourced_power2, ts_sourced_price2 = pf.dev.wp_sourced(\n",
+    "    ts_offtake, \"MS\", 0.2, p_avg=150\n",
+    ")\n",
+    "sourced_months = pf.PfLine({\"w\": ts_sourced_power2, \"p\": ts_sourced_price2})\n",
+    "sourced = pf.PfLine(\n",
+    "    {\"quarter_products\": sourced_quarters, \"month_products\": sourced_months}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now use these portfolio lines to create a portfolio state.\n",
+    "\n",
+    "## Portfolio State\n",
+    "\n",
+    "The ``PfState`` class is used to hold information about offtake, market prices, and sourcing. Let's create one from the portfolio lines we just created:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PfState object.\n",
+       ". Start: 2024-09-20 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+       ". End  : 2024-12-10 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+       ". Freq : <15 * Minutes> (7780 datapoints)\n",
+       "                                                w           q           p             r\n",
+       "                                               MW         MWh     Eur/MWh           Eur\n",
+       "──────── offtake\n",
+       "           2024-09-20 00:00:00 +0200        -45.8         -11                          \n",
+       "           2024-09-20 00:15:00 +0200        -44.4         -11                          \n",
+       "           ..                                  ..          ..          ..            ..\n",
+       "           2024-12-09 23:30:00 +0100        -60.5         -15                          \n",
+       "           2024-12-09 23:45:00 +0100        -58.3         -15                          \n",
+       "─●────── pnl_cost\n",
+       " │         2024-09-20 00:00:00 +0200         45.8          11      136.54         1 564\n",
+       " │         2024-09-20 00:15:00 +0200         44.4          11      135.21         1 500\n",
+       " │         ..                                  ..          ..          ..            ..\n",
+       " │         2024-12-09 23:30:00 +0100         60.5          15      167.89         2 540\n",
+       " │         2024-12-09 23:45:00 +0100         58.3          15      167.21         2 436\n",
+       " ├●───── sourced\n",
+       " ││        2024-09-20 00:00:00 +0200         31.3           8      135.03         1 058\n",
+       " ││        2024-09-20 00:15:00 +0200         31.3           8      135.03         1 058\n",
+       " ││        ..                                  ..          ..          ..            ..\n",
+       " ││        2024-12-09 23:30:00 +0100         35.7           9      126.09         1 124\n",
+       " ││        2024-12-09 23:45:00 +0100         35.7           9      126.09         1 124\n",
+       " │├───── quarter_products\n",
+       " ││        2024-09-20 00:00:00 +0200         16.4           4      102.48           421\n",
+       " ││        2024-09-20 00:15:00 +0200         16.4           4      102.48           421\n",
+       " ││        ..                                  ..          ..          ..            ..\n",
+       " ││        2024-12-09 23:30:00 +0100         13.4           3      111.14           372\n",
+       " ││        2024-12-09 23:45:00 +0100         13.4           3      111.14           372\n",
+       " │└───── month_products\n",
+       " │         2024-09-20 00:00:00 +0200         14.9           4      170.85           637\n",
+       " │         2024-09-20 00:15:00 +0200         14.9           4      170.85           637\n",
+       " │         ..                                  ..          ..          ..            ..\n",
+       " │         2024-12-09 23:30:00 +0100         22.3           6      135.07           752\n",
+       " │         2024-12-09 23:45:00 +0100         22.3           6      135.07           752\n",
+       " └────── unsourced\n",
+       "           2024-09-20 00:00:00 +0200         14.5           4      139.81           506\n",
+       "           2024-09-20 00:15:00 +0200         13.0           3      135.64           442\n",
+       "           ..                                  ..          ..          ..            ..\n",
+       "           2024-12-09 23:30:00 +0100         24.8           6      227.88         1 416\n",
+       "           2024-12-09 23:45:00 +0100         22.6           6      232.05         1 312"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pfs = pf.PfState(offtake, prices, sourced)\n",
+    "\n",
+    "pfs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note from how these portfolio lines were created, that ``offtake`` has negative values. The sign conventions are discussed [here](../core/pfstate.rst#sign-conventions).\n",
+    "\n",
+    "This portfolio state contains values for every quarterhour in the specified time period. Let's see what features this class has, starting with two methods we already met when discussing the ``PfLine`` class.\n",
+    "\n",
+    "### Plotting\n",
+    "\n",
+    "Just as when working with portfolio lines, we can get a quick overview of the portfolio state with the ``.plot()`` method..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pfs.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "...and we can copy its data to the clipboard, or save it as an Excel workbook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfs.to_clipboard()\n",
+    "pfs.to_excel(\"portfolio_state.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the offtake, we can see the daily and weekly cycles, as well as a slow increase over the entire time period.\n",
+    "\n",
+    "This graph is a bit too detailed for most purposes, so let's look at the second method we know from ``PfLine``: resampling.\n",
+    "\n",
+    "### Resampling\n",
+    "\n",
+    "We might prefer to see hourly, daily, or monthly values instead of the quarterhourly values that are in ``pfs``. For this, we can resample the object with the ``.asfreq()`` method. In this code example, we also use the ``.print()`` method, which adds some helpful coloring to the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PfState object.\n",
+      ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+      ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+      ". Freq : <MonthBegin> (2 datapoints)\n",
+      "                                                w           q           p             r\n",
+      "                                               MW         MWh     Eur/MWh           Eur\n",
+      "\u001b[1m\u001b[37m──────── offtake\n",
+      "  \u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200        -54.7     -40 744                          \n",
+      "  \u001b[1m\u001b[37m         \u001b[0m2024-11-01 00:00:00 +0100        -59.4     -42 732                          \n",
+      "\u001b[1m\u001b[37m─\u001b[1m\u001b[33m●\u001b[1m\u001b[37m────── pnl_cost\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452\n",
+      " \u001b[1m\u001b[33m├\u001b[1m\u001b[36m●\u001b[1m\u001b[33m───── sourced\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         31.2      23 221      132.58     3 078 642\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-11-01 00:00:00 +0100         25.9      18 652      133.27     2 485 849\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m├───── quarter_products\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         13.8      10 256      106.54     1 092 646\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-11-01 00:00:00 +0100         13.7       9 897      106.78     1 056 810\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m└───── month_products\n",
+      " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         17.4      12 964      153.19     1 985 996\n",
+      " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-11-01 00:00:00 +0100         12.2       8 755      163.22     1 429 039\n",
+      " \u001b[1m\u001b[33m└────── unsourced\n",
+      "   \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         23.5      17 523      197.44     3 459 829\n",
+      "   \u001b[1m\u001b[33m        \u001b[0m2024-11-01 00:00:00 +0100         33.4      24 080      221.41     5 331 603\n"
+     ]
+    }
+   ],
+   "source": [
+    "pfs_monthly = pfs.asfreq(\"MS\")\n",
+    "pfs_monthly.print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note how the resampled output only contains those months, that are *entirely* included in the original data. ``pfl`` has data in September and December, but as these are not present in their entirety, they are dropped from ``pfl_monthly``.\n",
+    "\n",
+    "### On frequencies and unsourced prices\n",
+    "\n",
+    "There is one other important consequence of resampling: the unsourced prices are now *specific for this portfolio*. We can demonstrate this by creating a second portfolio state, using the *same price-forward curve* (but different offtake and sourced volume):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfs2 = pf.PfState(offtake * 1.5, prices, sourced * 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this shortest frequency, the unsourced prices are identical (namely, the price-forward curve). Let's create a dataframe with the prices to verify this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pfs</th>\n",
+       "      <th>pfs2</th>\n",
+       "      <th>hpfc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-09-20 00:00:00+02:00</th>\n",
+       "      <td>139.80568207211454</td>\n",
+       "      <td>139.80568207211454</td>\n",
+       "      <td>139.80568207211454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-09-20 00:15:00+02:00</th>\n",
+       "      <td>135.63901540544785</td>\n",
+       "      <td>135.63901540544785</td>\n",
+       "      <td>135.63901540544785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-09-20 00:30:00+02:00</th>\n",
+       "      <td>131.47234873878116</td>\n",
+       "      <td>131.47234873878116</td>\n",
+       "      <td>131.47234873878116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-09-20 00:45:00+02:00</th>\n",
+       "      <td>128.13901540544785</td>\n",
+       "      <td>128.13901540544785</td>\n",
+       "      <td>128.13901540544785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-09-20 01:00:00+02:00</th>\n",
+       "      <td>124.80568207211451</td>\n",
+       "      <td>124.80568207211451</td>\n",
+       "      <td>124.80568207211451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-12-09 22:45:00+01:00</th>\n",
+       "      <td>217.88078474854294</td>\n",
+       "      <td>217.88078474854294</td>\n",
+       "      <td>217.88078474854294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-12-09 23:00:00+01:00</th>\n",
+       "      <td>221.2141180818763</td>\n",
+       "      <td>221.2141180818763</td>\n",
+       "      <td>221.2141180818763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-12-09 23:15:00+01:00</th>\n",
+       "      <td>224.54745141520962</td>\n",
+       "      <td>224.54745141520962</td>\n",
+       "      <td>224.54745141520962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-12-09 23:30:00+01:00</th>\n",
+       "      <td>227.88078474854294</td>\n",
+       "      <td>227.88078474854294</td>\n",
+       "      <td>227.88078474854294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-12-09 23:45:00+01:00</th>\n",
+       "      <td>232.0474514152096</td>\n",
+       "      <td>232.0474514152096</td>\n",
+       "      <td>232.0474514152096</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7780 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          pfs                pfs2  \\\n",
+       "2024-09-20 00:00:00+02:00  139.80568207211454  139.80568207211454   \n",
+       "2024-09-20 00:15:00+02:00  135.63901540544785  135.63901540544785   \n",
+       "2024-09-20 00:30:00+02:00  131.47234873878116  131.47234873878116   \n",
+       "2024-09-20 00:45:00+02:00  128.13901540544785  128.13901540544785   \n",
+       "2024-09-20 01:00:00+02:00  124.80568207211451  124.80568207211451   \n",
+       "...                                       ...                 ...   \n",
+       "2024-12-09 22:45:00+01:00  217.88078474854294  217.88078474854294   \n",
+       "2024-12-09 23:00:00+01:00   221.2141180818763   221.2141180818763   \n",
+       "2024-12-09 23:15:00+01:00  224.54745141520962  224.54745141520962   \n",
+       "2024-12-09 23:30:00+01:00  227.88078474854294  227.88078474854294   \n",
+       "2024-12-09 23:45:00+01:00   232.0474514152096   232.0474514152096   \n",
+       "\n",
+       "                                         hpfc  \n",
+       "2024-09-20 00:00:00+02:00  139.80568207211454  \n",
+       "2024-09-20 00:15:00+02:00  135.63901540544785  \n",
+       "2024-09-20 00:30:00+02:00  131.47234873878116  \n",
+       "2024-09-20 00:45:00+02:00  128.13901540544785  \n",
+       "2024-09-20 01:00:00+02:00  124.80568207211451  \n",
+       "...                                       ...  \n",
+       "2024-12-09 22:45:00+01:00  217.88078474854294  \n",
+       "2024-12-09 23:00:00+01:00   221.2141180818763  \n",
+       "2024-12-09 23:15:00+01:00  224.54745141520962  \n",
+       "2024-12-09 23:30:00+01:00  227.88078474854294  \n",
+       "2024-12-09 23:45:00+01:00   232.0474514152096  \n",
+       "\n",
+       "[7780 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(\n",
+    "    {\"pfs\": pfs.unsourcedprice.p, \"pfs2\": pfs2.unsourcedprice.p, \"hpfc\": prices.p}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, at every other frequency, they are not equal. When changing the frequency, a *volume-weighted* average is calculated for the unsourced prices - just like with every other price-and-volume timeseries. This makes that the unsourced prices apply only to the unsourced volume profile *of that portfolio*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pfs</th>\n",
+       "      <th>pfs2</th>\n",
+       "      <th>hpfc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-10-01 00:00:00+02:00</th>\n",
+       "      <td>197.4437803760265</td>\n",
+       "      <td>192.11463549822653</td>\n",
+       "      <td>194.78888729464586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-11-01 00:00:00+01:00</th>\n",
+       "      <td>221.40989836363784</td>\n",
+       "      <td>217.79829542112196</td>\n",
+       "      <td>220.07479863376392</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          pfs                pfs2  \\\n",
+       "2024-10-01 00:00:00+02:00   197.4437803760265  192.11463549822653   \n",
+       "2024-11-01 00:00:00+01:00  221.40989836363784  217.79829542112196   \n",
+       "\n",
+       "                                         hpfc  \n",
+       "2024-10-01 00:00:00+02:00  194.78888729464586  \n",
+       "2024-11-01 00:00:00+01:00  220.07479863376392  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(\n",
+    "    {\n",
+    "        \"pfs\": pfs.asfreq(\"MS\").unsourcedprice.p,\n",
+    "        \"pfs2\": pfs2.asfreq(\"MS\").unsourcedprice.p,\n",
+    "        \"hpfc\": prices.asfreq(\"MS\").p,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This has important consequences. \n",
+    "\n",
+    "For example, when doing a scenario analysis in which the unsourced volume is changed (e.g. \"what happens if the offtake increases by 50%?\"), we cannot expect the results to be correct *unless we are working at the original frequency*. In situations where it is clear that this error looms, a ``UserWarning`` is shown to alert the user (e.g. in the examples further below). For more information on unsourced volume, [see this section](../core/pfstate.rst#Unsourced-price) in the documentation on the `PfState` class.\n",
+    "\n",
+    "For this reason, we commonly work with our porfolio states at the frequency of the price-forward curve. Downsampling is only done to see the aggregated values for verification or reporting."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Components\n",
+    "\n",
+    "Now, let's look at the portfolio state ``pfs_monthly`` in a bit more detail, to learn more about the ``PfState`` class.\n",
+    "\n",
+    "The portfolio state is presented to us as a tree structure, with several branches. Each branch is a portfolio line. E.g, ``offtake`` and ``sourced`` are the portfolio lines we specified when creating the object. Also, the branch ``pnl_cost`` is the sum of ``sourced`` and ``unsourced``, with ``sourced`` being the sum of ``quarter_products`` and ``month_products``. \n",
+    "\n",
+    "The unsourced volume is found by comparing the offtake to what is already sourced. This volume is valued at the market prices in the forward curve.\n",
+    "\n",
+    "These portfolio lines can be obtained from the portfolio state by accessing them as attributes. E.g. ``.offtakevolume``, ``.sourced``, ``.unsourced``, or ``.pnl_cost``. The latter is the best estimate for what it will cost to procure the offtake:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PfLine object with price and volume information.\n",
+       ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+       ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+       ". Freq : <MonthBegin> (2 datapoints)\n",
+       ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
+       "                                     w           q           p             r\n",
+       "                                    MW         MWh     Eur/MWh           Eur\n",
+       "\n",
+       "2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
+       "2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pfs_monthly.pnl_cost"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that this portfolio line has children, and as a reminder, we can \"drill into\" the object to get these nested portfolio line, e.g. with ``pfl_monthly.pnl_cost[\"sourced\"]``.\n",
+    "\n",
+    "There are some other components that are not explicitly shown:\n",
+    "\n",
+    "* We may be interested in how much of the offtake has already been sourced or unsourced. These fractions are available at the ``.sourcedfraction`` and ``.unsourcedfraction`` properties.\n",
+    "\n",
+    "* You may have noticed that ``unsourced`` is the inverse from what traders would call the \"open positions\" or \"portfolio positions\": if our portfolio is short, the unsourced volume is positive. For those that prefer this other perspective, it is available at ``.netposition``.\n",
+    "\n",
+    "### Export\n",
+    "\n",
+    "Just as with portfolio lines, we can create an excel file that contains all the information in a portfolio state with its ``.to_excel()`` method, and we can copy it to the clipboard with the ``.to_clipboard()`` method.\n",
+    "\n",
+    "### MtM\n",
+    "\n",
+    "We can evaluate the value of our sourcing contracts against the current forward curve (\"mark-to-market\") with the ``.mtm_of_sourced()`` method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyses with portfolio states\n",
+    "\n",
+    "We'll now look at how we can do \"what-if\" analyses with portfolio state. The original portfolio state we will consider as the reference and store it in an appropriately named variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ref = pfs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The monthly procurement prices of this portfolio are what interest us the most. As a reminder, we can find the procurement volumes and costs with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PfLine object with price and volume information.\n",
+       ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+       ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+       ". Freq : <MonthBegin> (2 datapoints)\n",
+       ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
+       "                                     w           q           p             r\n",
+       "                                    MW         MWh     Eur/MWh           Eur\n",
+       "\n",
+       "2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
+       "2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_ref = ref.asfreq(\"MS\").pnl_cost\n",
+    "cost_ref"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Or we could go one step further and focus on only the prices with ``ref.asfreq(\"MS\").pnl_cost.p``.)\n",
+    "\n",
+    "### Change in offtake\n",
+    "\n",
+    "Now, what would happen if the offtake were to increase by 25%? Qualitatively, this is not hard. An increase in the offtake increases the unsourced volume. And because the market prices are higher than what we pay for the sourced volume, this means that the procurement price will go up. \n",
+    "\n",
+    "How much? Let's see. First, we create a new portfolio state, from the reference, by setting the offtake to the new value. We can do this with the ``.set_offtake()`` method. After that, we can again see what the procurement volumes and costs are. (Note the ``UserWarning`` which was mentioned [above](#on-frequencies-and-unsourced-pricesE).)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:199: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
+      "  warnings.warn(\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
+    {
+     "data": {
+      "text/plain": [
+       "PfLine object with price and volume information.\n",
+       ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+       ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+       ". Freq : <MonthBegin> (2 datapoints)\n",
+       ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
+       "                                     w           q           p             r\n",
+       "                                    MW         MWh     Eur/MWh           Eur\n",
+       "\n",
+       "2024-10-01 00:00:00 +0200         68.4      50 930      168.64     8 588 719\n",
+       "2024-11-01 00:00:00 +0100         74.2      53 416      191.54    10 231 182"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "higherofftake = ref.offtakevolume * 1.25\n",
+    "pfs_higherofftake = ref.set_offtakevolume(higherofftake)\n",
+    "cost_higherofftake = pfs_higherofftake.asfreq(\"MS\").pnl_cost\n",
+    "cost_higherofftake"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comparing these two ``cost`` portfolio lines, we see that indeed the values for ``w`` and ``q`` have increased to 125% of the original values. Also, the procurement prices have increased. We can quickly calculate by how much:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2024-10-01 00:00:00+02:00    8.160879013401626\n",
+       "2024-11-01 00:00:00+01:00    8.599884206454362\n",
+       "Freq: MS, Name: p, dtype: pint[Eur/MWh]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_higherofftake.p - cost_ref.p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could similarly create a portfolio states for situations with a market price drop of 40%. Or one which combines both effects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:199: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "lowerprices = ref.unsourcedprice * 0.6\n",
+    "pfs_lowerprices = ref.set_unsourcedprice(lowerprices)\n",
+    "pfs_lowerprices_higherofftake = ref.set_offtakevolume(higherofftake).set_unsourcedprice(\n",
+    "    lowerprices\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hedging\n",
+    "\n",
+    "Hedging can reduce the sensitivity of our portfolio to changes in the market price. Given the current market price curve, we can calculate how much we'd need to source to obtain a fully hedged portfolio:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "needed = ref.hedge_of_unsourced(\"val\", \"MS\")  # value hedge with month products"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say we procure exactly that volume. We can add it to the sourced volume in our portfolio state:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:209: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "hedged = ref.add_sourced(pf.PfLine({\"newvolume\": needed}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(We could have obtained the same result with the ``ref.source_unsourced()`` method.)\n",
+    "\n",
+    "The portfolio is now hedged at the month level. We can verify this by looking at the unsourced volume. In case of a volume hedge, the unsourced volume (``q`` and ``w``) is 0, even if its monetary value (``r``) is not; in case of a value hedge, it is the reverse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PfLine object with price and volume information.\n",
+       ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+       ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+       ". Freq : <MonthBegin> (2 datapoints)\n",
+       "                                     w           q           p             r\n",
+       "                                    MW         MWh     Eur/MWh           Eur\n",
+       "\n",
+       "2024-10-01 00:00:00 +0200         -0.2        -173        0.00            -0\n",
+       "2024-11-01 00:00:00 +0100         -0.2        -134        0.00            -0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hedged.asfreq(\"MS\").unsourced"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the market prices have not changed, the best-estimate procurement prices (at month level and longer) are also unchanged from before. (This is verified in the \"before\" columns of the dataframe further below.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Market price change\n",
+    "\n",
+    "A hedged profile is less impacted by market price changes. To see that this is indeed the case, let's look at a scenario with an increase in the forward price curve by 40 Eur/MWh, for both portfolio states:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newprices = prices + pf.Q_(40.0, \"Eur/MWh\")\n",
+    "ref_higherprices = ref.set_unsourcedprice(newprices)\n",
+    "hedged_higherprices = hedged.set_unsourcedprice(newprices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The reference portfolio has gotten a lot more expensive, whereas the procurement price for the hedged portfolio has not moved significantly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">ref</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">hedged</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>before</th>\n",
+       "      <th>after</th>\n",
+       "      <th>before</th>\n",
+       "      <th>after</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unit</th>\n",
+       "      <th>Eur/MWh</th>\n",
+       "      <th>Eur/MWh</th>\n",
+       "      <th>Eur/MWh</th>\n",
+       "      <th>Eur/MWh</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-10-01 00:00:00+02:00</th>\n",
+       "      <td>160.478106</td>\n",
+       "      <td>177.681366</td>\n",
+       "      <td>160.478106</td>\n",
+       "      <td>160.307936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-11-01 00:00:00+01:00</th>\n",
+       "      <td>182.939602</td>\n",
+       "      <td>205.480086</td>\n",
+       "      <td>182.939602</td>\n",
+       "      <td>182.814260</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  ref                  hedged            \n",
+       "                               before       after      before       after\n",
+       "unit                          Eur/MWh     Eur/MWh     Eur/MWh     Eur/MWh\n",
+       "2024-10-01 00:00:00+02:00  160.478106  177.681366  160.478106  160.307936\n",
+       "2024-11-01 00:00:00+01:00  182.939602  205.480086  182.939602  182.814260"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(\n",
+    "    {\n",
+    "        (\"ref\", \"before\"): ref.pnl_cost.asfreq(\"MS\").p,\n",
+    "        (\"ref\", \"after\"): ref_higherprices.pnl_cost.asfreq(\"MS\").p,\n",
+    "        (\"hedged\", \"before\"): hedged.pnl_cost.asfreq(\"MS\").p,\n",
+    "        (\"hedged\", \"after\"): hedged_higherprices.pnl_cost.asfreq(\"MS\").p,\n",
+    "    }\n",
+    ").pint.dequantify()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the observant reader: it may seem that the portfolio was not fully hedged after all, as a small change in the procurement price is seen. The reason is that each strategy (i.e., volume or value hedge) fully protects only against a specific price change (i.e., absolute or relative). A volume hedge does not *fully* hedge against an absolute price change such as the one we see here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tutorial is continued [in part 4](part4.ipynb)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 ('pf38')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "642a4be8010ca5d45039b988c1d8379a91572488c4d23a0b88e966c6713c7e45"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/docs/tutorial/part4.ipynb b/docs/tutorial/part4.ipynb
index eee3669..0a323da 100644
--- a/docs/tutorial/part4.ipynb
+++ b/docs/tutorial/part4.ipynb
@@ -1,282 +1,282 @@
 {
-   "cells": [
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "# Tutorial part 4\n",
-            "\n",
-            "In [part 3](part3.ipynb) we have learnt about portfolio states and how to use them in scenario analyses. Here we learn how to export them and how to combine several ones.\n",
-            "\n",
-            "## Example data\n",
-            "\n",
-            "We start with a similar portfolio state as in the previous part:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 1,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "PfState object.\n",
-                  ". Start: 2024-09-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                  ". End  : 2024-11-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                  ". Freq : <MonthBegin> (2 datapoints)\n",
-                  "                                                w           q           p             r\n",
-                  "                                               MW         MWh     Eur/MWh           Eur\n",
-                  "\u001b[1m\u001b[37m──────── offtake\n",
-                  "  \u001b[1m\u001b[37m         \u001b[0m2024-09-01 00:00:00 +0200        -49.4     -35 593                          \n",
-                  "  \u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200        -54.7     -40 748                          \n",
-                  "\u001b[1m\u001b[37m─\u001b[1m\u001b[33m●\u001b[1m\u001b[37m────── pnl_cost\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-09-01 00:00:00 +0200         49.4      35 593      147.61     5 253 964\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200         54.7      40 748      163.85     6 676 561\n",
-                  " \u001b[1m\u001b[33m├\u001b[1m\u001b[36m●\u001b[1m\u001b[33m───── sourced\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-09-01 00:00:00 +0200         28.9      20 844      123.03     2 564 413\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         26.0      19 389      132.10     2 561 236\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m├───── quarter_products\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-09-01 00:00:00 +0200         13.8       9 943      103.24     1 026 519\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         11.1       8 261      118.15       976 055\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m└───── month_products\n",
-                  " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-09-01 00:00:00 +0200         15.1      10 901      141.07     1 537 894\n",
-                  " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         14.9      11 128      142.45     1 585 180\n",
-                  " \u001b[1m\u001b[33m└────── unsourced\n",
-                  "   \u001b[1m\u001b[33m        \u001b[0m2024-09-01 00:00:00 +0200         20.5      14 749      182.36     2 689 551\n",
-                  "   \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         28.7      21 358      192.68     4 115 325\n"
-               ]
-            }
-         ],
-         "source": [
-            "import portfolyo as pf\n",
-            "import pandas as pd\n",
-            "\n",
-            "index = pd.date_range(\n",
-            "    \"2024-09-01\", \"2024-11-01\", freq=\"15T\", inclusive=\"left\", tz=\"Europe/Berlin\"\n",
-            ")\n",
-            "# Creating portfolio line with market prices (here: price-forward curve).\n",
-            "ts_prices = pf.dev.p_marketprices(index, avg=200)\n",
-            "prices = pf.PfLine({\"p\": ts_prices})\n",
-            "\n",
-            "\n",
-            "# Creating offtake portfolio line.\n",
-            "ts_offtake = -1 * pf.dev.w_offtake(index, avg=50)\n",
-            "offtake = pf.PfLine({\"w\": ts_offtake})\n",
-            "\n",
-            "# Creating portfolio line with sourced volume.\n",
-            "ts_sourced_power1, ts_sourced_price1 = pf.dev.wp_sourced(\n",
-            "    ts_offtake, \"QS\", 0.3, p_avg=120\n",
-            ")\n",
-            "sourced_quarters = pf.PfLine({\"w\": ts_sourced_power1, \"p\": ts_sourced_price1})\n",
-            "ts_sourced_power2, ts_sourced_price2 = pf.dev.wp_sourced(\n",
-            "    ts_offtake, \"MS\", 0.2, p_avg=150\n",
-            ")\n",
-            "sourced_months = pf.PfLine({\"w\": ts_sourced_power2, \"p\": ts_sourced_price2})\n",
-            "sourced = pf.PfLine(\n",
-            "    {\"quarter_products\": sourced_quarters, \"month_products\": sourced_months}\n",
-            ")\n",
-            "\n",
-            "# Create the portfolio state.\n",
-            "pfs1 = pf.PfState(offtake, prices, sourced).asfreq('MS')\n",
-            "\n",
-            "pfs1.print()"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "## Arithmatic\n",
-            "\n",
-            "The final part about portfolio lines is the arithmatic that can be done with them.\n",
-            "\n",
-            "Let's create a second portfolio state:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 2,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "PfState object.\n",
-                  ". Start: 2024-09-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                  ". End  : 2024-11-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                  ". Freq : <MonthBegin> (2 datapoints)\n",
-                  "                                                w           q           p             r\n",
-                  "                                               MW         MWh     Eur/MWh           Eur\n",
-                  "\u001b[1m\u001b[37m──────── offtake\n",
-                  "  \u001b[1m\u001b[37m         \u001b[0m2024-09-01 00:00:00 +0200        -98.9     -71 186                          \n",
-                  "  \u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200       -109.4     -81 495                          \n",
-                  "\u001b[1m\u001b[37m─\u001b[1m\u001b[33m●\u001b[1m\u001b[37m────── pnl_cost\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-09-01 00:00:00 +0200         98.9      71 186      118.77     8 454 414\n",
-                  " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200        109.4      81 495      131.98    10 755 857\n",
-                  " \u001b[1m\u001b[33m├────── sourced\n",
-                  " \u001b[1m\u001b[33m│ \u001b[1m\u001b[33m        \u001b[0m2024-09-01 00:00:00 +0200         80.0      57 600      100.00     5 760 000\n",
-                  " \u001b[1m\u001b[33m│ \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         80.0      59 600      100.00     5 960 000\n",
-                  " \u001b[1m\u001b[33m└────── unsourced\n",
-                  "   \u001b[1m\u001b[33m        \u001b[0m2024-09-01 00:00:00 +0200         18.9      13 586      198.33     2 694 414\n",
-                  "   \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         29.4      21 895      219.04     4 795 857\n"
-               ]
-            }
-         ],
-         "source": [
-            "offtake2 = offtake * 2\n",
-            "sourced2 = pf.PfLine(pd.DataFrame({\"w\": 80, \"p\": 100}, index))\n",
-            "pfs2 = pf.PfState(offtake2, prices, sourced2).asfreq('MS')\n",
-            "pfs2.print()"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "Note that ``pfs1`` and ``pfs2`` have distinct unsourced prices at this month level, even though they were created using the same market prices on the quarter-hour level.\n",
-            "\n",
-            "### Addition and subtraction\n",
-            "\n",
-            "We can add these two portfolio states:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 3,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stderr",
-               "output_type": "stream",
-               "text": [
-                  "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfline\\enable_arithmatic.py:82: PfLineFlattenedWarning: When adding a FlatPfLine and NestedPfLine, the NestedPfLine is flattened.\n",
-                  "  warnings.warn(\n"
-               ]
-            },
-            {
-               "data": {
-                  "text/plain": [
-                     "PfState object.\n",
-                     ". Start: 2024-09-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                     ". End  : 2024-11-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                     ". Freq : <MonthBegin> (2 datapoints)\n",
-                     "                                                w           q           p             r\n",
-                     "                                               MW         MWh     Eur/MWh           Eur\n",
-                     "──────── offtake\n",
-                     "           2024-09-01 00:00:00 +0200       -148.3    -106 778                          \n",
-                     "           2024-10-01 00:00:00 +0200       -164.1    -122 243                          \n",
-                     "─●────── pnl_cost\n",
-                     " │         2024-09-01 00:00:00 +0200        148.3     106 778      128.38    13 708 378\n",
-                     " │         2024-10-01 00:00:00 +0200        164.1     122 243      142.60    17 432 418\n",
-                     " ├────── sourced\n",
-                     " │         2024-09-01 00:00:00 +0200        108.9      78 444      106.12     8 324 413\n",
-                     " │         2024-10-01 00:00:00 +0200        106.0      78 989      107.88     8 521 236\n",
-                     " └────── unsourced\n",
-                     "           2024-09-01 00:00:00 +0200         39.4      28 334      190.02     5 383 965\n",
-                     "           2024-10-01 00:00:00 +0200         58.1      43 254      206.02     8 911 182"
-                  ]
-               },
-               "execution_count": 3,
-               "metadata": {},
-               "output_type": "execute_result"
-            }
-         ],
-         "source": [
-            "pfs1 + pfs2"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "Note that the individual components are added together. The volumes (offtake, sourced, unsourced) are summed; the prices (sourced and unsourced) are the energy-weighted averaged. (Or, put differently, the *revenues* are also summed, and the prices are calculated from the volume-total and renevue-total.)\n",
-            "\n",
-            "Note also that the sourced volume of ``pfs1`` has been flattened, i.e., the values of its children are lost. This is because ``pfs2`` does not have any children. This behaviour is described [here](../core/pfline.rst#Arithmatic).\n",
-            "\n",
-            "Likewise we can subtract them with ``pfs1 - pfs2``:\n"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 4,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stderr",
-               "output_type": "stream",
-               "text": [
-                  "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfline\\enable_arithmatic.py:82: PfLineFlattenedWarning: When adding a FlatPfLine and NestedPfLine, the NestedPfLine is flattened.\n",
-                  "  warnings.warn(\n"
-               ]
-            },
-            {
-               "data": {
-                  "text/plain": [
-                     "PfState object.\n",
-                     ". Start: 2024-09-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-                     ". End  : 2024-11-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-                     ". Freq : <MonthBegin> (2 datapoints)\n",
-                     "                                                w           q           p             r\n",
-                     "                                               MW         MWh     Eur/MWh           Eur\n",
-                     "──────── offtake\n",
-                     "           2024-09-01 00:00:00 +0200         49.4      35 593                          \n",
-                     "           2024-10-01 00:00:00 +0200         54.7      40 748                          \n",
-                     "─●────── pnl_cost\n",
-                     " │         2024-09-01 00:00:00 +0200        -49.4     -35 593       89.92    -3 200 450\n",
-                     " │         2024-10-01 00:00:00 +0200        -54.7     -40 748      100.11    -4 079 296\n",
-                     " ├────── sourced\n",
-                     " │         2024-09-01 00:00:00 +0200        -51.1     -36 756       86.94    -3 195 587\n",
-                     " │         2024-10-01 00:00:00 +0200        -54.0     -40 211       84.52    -3 398 764\n",
-                     " └────── unsourced\n",
-                     "           2024-09-01 00:00:00 +0200          1.6       1 163       -4.18        -4 863\n",
-                     "           2024-10-01 00:00:00 +0200         -0.7        -537    1 267.81      -680 532"
-                  ]
-               },
-               "execution_count": 4,
-               "metadata": {},
-               "output_type": "execute_result"
-            }
-         ],
-         "source": [
-            "pfs1 - pfs2"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "That was it for this tutorial!"
-         ]
-      }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial part 4\n",
+    "\n",
+    "In [part 3](part3.ipynb) we have learnt about portfolio states and how to use them in scenario analyses. Here we learn how to export them and how to combine several ones.\n",
+    "\n",
+    "## Example data\n",
+    "\n",
+    "We start with a similar portfolio state as in the previous part:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PfState object.\n",
+      ". Start: 2024-09-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+      ". End  : 2024-11-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+      ". Freq : <MonthBegin> (2 datapoints)\n",
+      "                                                w           q           p             r\n",
+      "                                               MW         MWh     Eur/MWh           Eur\n",
+      "\u001b[1m\u001b[37m──────── offtake\n",
+      "  \u001b[1m\u001b[37m         \u001b[0m2024-09-01 00:00:00 +0200        -49.4     -35 593                          \n",
+      "  \u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200        -54.7     -40 748                          \n",
+      "\u001b[1m\u001b[37m─\u001b[1m\u001b[33m●\u001b[1m\u001b[37m────── pnl_cost\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-09-01 00:00:00 +0200         49.4      35 593      147.61     5 253 964\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200         54.7      40 748      163.85     6 676 561\n",
+      " \u001b[1m\u001b[33m├\u001b[1m\u001b[36m●\u001b[1m\u001b[33m───── sourced\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-09-01 00:00:00 +0200         28.9      20 844      123.03     2 564 413\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         26.0      19 389      132.10     2 561 236\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m├───── quarter_products\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-09-01 00:00:00 +0200         13.8       9 943      103.24     1 026 519\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         11.1       8 261      118.15       976 055\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m└───── month_products\n",
+      " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-09-01 00:00:00 +0200         15.1      10 901      141.07     1 537 894\n",
+      " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         14.9      11 128      142.45     1 585 180\n",
+      " \u001b[1m\u001b[33m└────── unsourced\n",
+      "   \u001b[1m\u001b[33m        \u001b[0m2024-09-01 00:00:00 +0200         20.5      14 749      182.36     2 689 551\n",
+      "   \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         28.7      21 358      192.68     4 115 325\n"
+     ]
+    }
    ],
-   "metadata": {
-      "kernelspec": {
-         "display_name": "Python 3.8.13 ('pf38')",
-         "language": "python",
-         "name": "python3"
-      },
-      "language_info": {
-         "codemirror_mode": {
-            "name": "ipython",
-            "version": 3
-         },
-         "file_extension": ".py",
-         "mimetype": "text/x-python",
-         "name": "python",
-         "nbconvert_exporter": "python",
-         "pygments_lexer": "ipython3",
-         "version": "3.8.13"
-      },
-      "orig_nbformat": 4,
-      "vscode": {
-         "interpreter": {
-            "hash": "642a4be8010ca5d45039b988c1d8379a91572488c4d23a0b88e966c6713c7e45"
-         }
-      }
+   "source": [
+    "import portfolyo as pf\n",
+    "import pandas as pd\n",
+    "\n",
+    "index = pd.date_range(\n",
+    "    \"2024-09-01\", \"2024-11-01\", freq=\"15T\", inclusive=\"left\", tz=\"Europe/Berlin\"\n",
+    ")\n",
+    "# Creating portfolio line with market prices (here: price-forward curve).\n",
+    "ts_prices = pf.dev.p_marketprices(index, avg=200)\n",
+    "prices = pf.PfLine({\"p\": ts_prices})\n",
+    "\n",
+    "\n",
+    "# Creating offtake portfolio line.\n",
+    "ts_offtake = -1 * pf.dev.w_offtake(index, avg=50)\n",
+    "offtake = pf.PfLine({\"w\": ts_offtake})\n",
+    "\n",
+    "# Creating portfolio line with sourced volume.\n",
+    "ts_sourced_power1, ts_sourced_price1 = pf.dev.wp_sourced(\n",
+    "    ts_offtake, \"QS\", 0.3, p_avg=120\n",
+    ")\n",
+    "sourced_quarters = pf.PfLine({\"w\": ts_sourced_power1, \"p\": ts_sourced_price1})\n",
+    "ts_sourced_power2, ts_sourced_price2 = pf.dev.wp_sourced(\n",
+    "    ts_offtake, \"MS\", 0.2, p_avg=150\n",
+    ")\n",
+    "sourced_months = pf.PfLine({\"w\": ts_sourced_power2, \"p\": ts_sourced_price2})\n",
+    "sourced = pf.PfLine(\n",
+    "    {\"quarter_products\": sourced_quarters, \"month_products\": sourced_months}\n",
+    ")\n",
+    "\n",
+    "# Create the portfolio state.\n",
+    "pfs1 = pf.PfState(offtake, prices, sourced).asfreq(\"MS\")\n",
+    "\n",
+    "pfs1.print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Arithmatic\n",
+    "\n",
+    "The final part about portfolio lines is the arithmatic that can be done with them.\n",
+    "\n",
+    "Let's create a second portfolio state:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PfState object.\n",
+      ". Start: 2024-09-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+      ". End  : 2024-11-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+      ". Freq : <MonthBegin> (2 datapoints)\n",
+      "                                                w           q           p             r\n",
+      "                                               MW         MWh     Eur/MWh           Eur\n",
+      "\u001b[1m\u001b[37m──────── offtake\n",
+      "  \u001b[1m\u001b[37m         \u001b[0m2024-09-01 00:00:00 +0200        -98.9     -71 186                          \n",
+      "  \u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200       -109.4     -81 495                          \n",
+      "\u001b[1m\u001b[37m─\u001b[1m\u001b[33m●\u001b[1m\u001b[37m────── pnl_cost\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-09-01 00:00:00 +0200         98.9      71 186      118.77     8 454 414\n",
+      " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200        109.4      81 495      131.98    10 755 857\n",
+      " \u001b[1m\u001b[33m├────── sourced\n",
+      " \u001b[1m\u001b[33m│ \u001b[1m\u001b[33m        \u001b[0m2024-09-01 00:00:00 +0200         80.0      57 600      100.00     5 760 000\n",
+      " \u001b[1m\u001b[33m│ \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         80.0      59 600      100.00     5 960 000\n",
+      " \u001b[1m\u001b[33m└────── unsourced\n",
+      "   \u001b[1m\u001b[33m        \u001b[0m2024-09-01 00:00:00 +0200         18.9      13 586      198.33     2 694 414\n",
+      "   \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         29.4      21 895      219.04     4 795 857\n"
+     ]
+    }
+   ],
+   "source": [
+    "offtake2 = offtake * 2\n",
+    "sourced2 = pf.PfLine(pd.DataFrame({\"w\": 80, \"p\": 100}, index))\n",
+    "pfs2 = pf.PfState(offtake2, prices, sourced2).asfreq(\"MS\")\n",
+    "pfs2.print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that ``pfs1`` and ``pfs2`` have distinct unsourced prices at this month level, even though they were created using the same market prices on the quarter-hour level.\n",
+    "\n",
+    "### Addition and subtraction\n",
+    "\n",
+    "We can add these two portfolio states:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfline\\enable_arithmatic.py:82: PfLineFlattenedWarning: When adding a FlatPfLine and NestedPfLine, the NestedPfLine is flattened.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PfState object.\n",
+       ". Start: 2024-09-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+       ". End  : 2024-11-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+       ". Freq : <MonthBegin> (2 datapoints)\n",
+       "                                                w           q           p             r\n",
+       "                                               MW         MWh     Eur/MWh           Eur\n",
+       "──────── offtake\n",
+       "           2024-09-01 00:00:00 +0200       -148.3    -106 778                          \n",
+       "           2024-10-01 00:00:00 +0200       -164.1    -122 243                          \n",
+       "─●────── pnl_cost\n",
+       " │         2024-09-01 00:00:00 +0200        148.3     106 778      128.38    13 708 378\n",
+       " │         2024-10-01 00:00:00 +0200        164.1     122 243      142.60    17 432 418\n",
+       " ├────── sourced\n",
+       " │         2024-09-01 00:00:00 +0200        108.9      78 444      106.12     8 324 413\n",
+       " │         2024-10-01 00:00:00 +0200        106.0      78 989      107.88     8 521 236\n",
+       " └────── unsourced\n",
+       "           2024-09-01 00:00:00 +0200         39.4      28 334      190.02     5 383 965\n",
+       "           2024-10-01 00:00:00 +0200         58.1      43 254      206.02     8 911 182"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pfs1 + pfs2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the individual components are added together. The volumes (offtake, sourced, unsourced) are summed; the prices (sourced and unsourced) are the energy-weighted averaged. (Or, put differently, the *revenues* are also summed, and the prices are calculated from the volume-total and renevue-total.)\n",
+    "\n",
+    "Note also that the sourced volume of ``pfs1`` has been flattened, i.e., the values of its children are lost. This is because ``pfs2`` does not have any children. This behaviour is described [here](../core/pfline.rst#Arithmatic).\n",
+    "\n",
+    "Likewise we can subtract them with ``pfs1 - pfs2``:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfline\\enable_arithmatic.py:82: PfLineFlattenedWarning: When adding a FlatPfLine and NestedPfLine, the NestedPfLine is flattened.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PfState object.\n",
+       ". Start: 2024-09-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+       ". End  : 2024-11-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+       ". Freq : <MonthBegin> (2 datapoints)\n",
+       "                                                w           q           p             r\n",
+       "                                               MW         MWh     Eur/MWh           Eur\n",
+       "──────── offtake\n",
+       "           2024-09-01 00:00:00 +0200         49.4      35 593                          \n",
+       "           2024-10-01 00:00:00 +0200         54.7      40 748                          \n",
+       "─●────── pnl_cost\n",
+       " │         2024-09-01 00:00:00 +0200        -49.4     -35 593       89.92    -3 200 450\n",
+       " │         2024-10-01 00:00:00 +0200        -54.7     -40 748      100.11    -4 079 296\n",
+       " ├────── sourced\n",
+       " │         2024-09-01 00:00:00 +0200        -51.1     -36 756       86.94    -3 195 587\n",
+       " │         2024-10-01 00:00:00 +0200        -54.0     -40 211       84.52    -3 398 764\n",
+       " └────── unsourced\n",
+       "           2024-09-01 00:00:00 +0200          1.6       1 163       -4.18        -4 863\n",
+       "           2024-10-01 00:00:00 +0200         -0.7        -537    1 267.81      -680 532"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pfs1 - pfs2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That was it for this tutorial!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 ('pf38')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
    },
-   "nbformat": 4,
-   "nbformat_minor": 2
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "642a4be8010ca5d45039b988c1d8379a91572488c4d23a0b88e966c6713c7e45"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
\ No newline at end of file
diff --git a/portfolyo/core/ndframelike.py b/portfolyo/core/ndframelike.py
index 560a576..60f4aa0 100644
--- a/portfolyo/core/ndframelike.py
+++ b/portfolyo/core/ndframelike.py
@@ -42,6 +42,12 @@ def loc(self):
         a boolean array.)"""
         ...
 
+    @abc.abstractproperty
+    def slice(self):
+        """Create a new instance with a subset of the rows.
+        Different from loc since performs slicing with right-open interval."""
+        ...
+
     @abc.abstractmethod
     def dataframe(
         self, cols: Iterable[str] = None, has_units: bool = True, *args, **kwargs
diff --git a/portfolyo/core/pfline/classes.py b/portfolyo/core/pfline/classes.py
index 8c6dd0b..a2aa855 100644
--- a/portfolyo/core/pfline/classes.py
+++ b/portfolyo/core/pfline/classes.py
@@ -286,6 +286,7 @@ class FlatPfLine(PfLine):
     hedge_with = prices.Flat.hedge_with
     # map_to_year => on child classes
     loc = flat_methods.loc
+    slice = flat_methods.slice
     __getitem__ = flat_methods.__getitem__
     # __bool__ => on child classes
     __eq__ = flat_methods.__eq__
@@ -300,6 +301,7 @@ class NestedPfLine(PfLine, children.ChildFunctionality):
     hedge_with = prices.Nested.hedge_with
     map_to_year = nested_methods.map_to_year
     loc = nested_methods.loc
+    slice = nested_methods.slice
     __bool__ = nested_methods.__bool__
     __eq__ = nested_methods.__eq__
 
diff --git a/portfolyo/core/pfline/flat_methods.py b/portfolyo/core/pfline/flat_methods.py
index 90a27e4..155ff0f 100644
--- a/portfolyo/core/pfline/flat_methods.py
+++ b/portfolyo/core/pfline/flat_methods.py
@@ -3,6 +3,8 @@
 from typing import TYPE_CHECKING, Any
 
 from ... import testing
+import pandas as pd
+from datetime import timedelta
 
 if TYPE_CHECKING:
     from .classes import FlatPfLine
@@ -31,6 +33,11 @@ def loc(self: FlatPfLine) -> LocIndexer:
     return LocIndexer(self)
 
 
+@property
+def slice(self: FlatPfLine) -> SliceIndexer:
+    return SliceIndexer(self)
+
+
 class LocIndexer:
     """Helper class to obtain FlatPfLine instance, whose index is subset of original index."""
 
@@ -40,3 +47,21 @@ def __init__(self, pfl: FlatPfLine):
     def __getitem__(self, arg) -> FlatPfLine:
         newdf = self.pfl.df.loc[arg]
         return self.pfl.__class__(newdf)  # use same (leaf) class
+
+
+class SliceIndexer:
+    """Helper class to obtain FlatPfLine instance, whose index is subset of original index.
+    Exclude end point from the slice."""
+
+    def __init__(self, pfl: FlatPfLine):
+        self.pfl = pfl
+
+    def __getitem__(self, arg) -> FlatPfLine:
+        date_start = pd.to_datetime(arg.start)
+        date_end = pd.to_datetime(arg.stop)
+
+        if arg.stop is not None:
+            date_end = date_end - timedelta(seconds=1)
+
+        newdf = self.pfl.df.loc[date_start:date_end]
+        return self.pfl.__class__(newdf)  # use same (leaf) class
diff --git a/portfolyo/core/pfline/nested_methods.py b/portfolyo/core/pfline/nested_methods.py
index dbe1c4b..33d37b1 100644
--- a/portfolyo/core/pfline/nested_methods.py
+++ b/portfolyo/core/pfline/nested_methods.py
@@ -37,6 +37,11 @@ def loc(self: NestedPfLine) -> LocIndexer:
     return LocIndexer(self)
 
 
+@property
+def slice(self: NestedPfLine) -> SliceIndexer:
+    return SliceIndexer(self)
+
+
 class LocIndexer:
     """Helper class to obtain NestedPfLine instance, whose index is subset of original index."""
 
@@ -46,3 +51,15 @@ def __init__(self, pfl: NestedPfLine):
     def __getitem__(self, arg) -> NestedPfLine:
         newchildren = {name: child.loc[arg] for name, child in self.pfl.items()}
         return self.pfl.__class__(newchildren)
+
+
+class SliceIndexer:
+    """Helper class to obtain NestedPfLine instance, whose index is subset of original index.
+    Exclude end point from the slice."""
+
+    def __init__(self, pfl: NestedPfLine):
+        self.pfl = pfl
+
+    def __getitem__(self, arg) -> NestedPfLine:
+        newchildren = {name: child.slice[arg] for name, child in self.pfl.items()}
+        return self.pfl.__class__(newchildren)
diff --git a/portfolyo/core/pfstate/pfstate.py b/portfolyo/core/pfstate/pfstate.py
index b90b79c..ed127a4 100644
--- a/portfolyo/core/pfstate/pfstate.py
+++ b/portfolyo/core/pfstate/pfstate.py
@@ -280,6 +280,10 @@ def __bool__(self):
     def loc(self) -> _LocIndexer:  # from ABC
         return _LocIndexer(self)
 
+    @property
+    def slice(self) -> _SliceIndexer:  # from ABC
+        return _SliceIndexer(self)
+
 
 class _LocIndexer:
     """Helper class to obtain PfState instance, whose index is subset of original index."""
@@ -292,3 +296,17 @@ def __getitem__(self, arg) -> PfState:
         unsourcedprice = self.pfs.unsourcedprice.loc[arg]
         sourced = self.pfs.sourced.loc[arg]
         return PfState(offtakevolume, unsourcedprice, sourced)
+
+
+class _SliceIndexer:
+    """Helper class to obtain PfState instance, whose index is subset of original index.
+    Exclude end index from the slice"""
+
+    def __init__(self, pfs):
+        self.pfs = pfs
+
+    def __getitem__(self, arg) -> PfState:
+        offtakevolume = self.pfs.offtake.volume.slice[arg]
+        unsourcedprice = self.pfs.unsourcedprice.slice[arg]
+        sourced = self.pfs.sourced.slice[arg]
+        return PfState(offtakevolume, unsourcedprice, sourced)
diff --git a/tests/core/pfline/test_slice.py b/tests/core/pfline/test_slice.py
new file mode 100644
index 0000000..d7b76e2
--- /dev/null
+++ b/tests/core/pfline/test_slice.py
@@ -0,0 +1,91 @@
+"""Test if slice attributes works properly with portfolio line."""
+
+import pytest
+import pandas as pd
+from portfolyo import dev
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize("slice_start", ["2021", "2022", "2022-01-02"])
+def test_flat_slice_start(slice_start, freq):
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfl1 = dev.get_flatpfline(index)
+    assert pfl1.slice[slice_start:] == pfl1.loc[slice_start:]
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize(
+    "slice_end",
+    [
+        # (<param for slice>, <param for loc>)
+        ("2021", "2020"),
+        ("2022", "2021"),
+        ("2021-07", "2021-06"),
+        ("2022-01-02", "2022-01-01"),
+    ],
+)
+def test_flat_slice_end(slice_end, freq):
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfl1 = dev.get_flatpfline(index)
+    assert pfl1.slice[: slice_end[0]] == pfl1.loc[: slice_end[1]]
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize(
+    "where",
+    ["2022", "2022-03", "2022-04-21", "2022-05-23 14:34"],
+)
+def test_flat_slice_whole(where: str, freq: str):
+    """Test that slicing splits the pfl in 2 non-overlapping pieces without gap
+    (i.e., ensure that each original timestamp is in exactly one of the resulting pieces.)
+    """
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfl1 = dev.get_flatpfline(index)
+    left, right = pfl1.slice[:where], pfl1.slice[where:]
+    # Test that each timestamp is present at least once.
+    pd.testing.assert_index_equal(left.index.union(right.index), index)
+    # Test that no timestamp is present twice.
+    assert len(left.index.intersection(right.index)) == 0
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize("slice_start", ["2021", "2022", "2022-01-02"])
+def test_nested_slice_start(slice_start, freq):
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfl1 = dev.get_nestedpfline(index)
+    assert pfl1.slice[slice_start:] == pfl1.loc[slice_start:]
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize(
+    "slice_end",
+    [
+        # (<param for slice>, <param for loc>)
+        ("2021", "2020"),
+        ("2022", "2021"),
+        ("2021-07", "2021-06"),
+        ("2022-01-02", "2022-01-01"),
+    ],
+)
+def test_nested_slice_end(slice_end, freq):
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfl1 = dev.get_nestedpfline(index)
+    assert pfl1.slice[: slice_end[0]] == pfl1.loc[: slice_end[1]]
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize(
+    "where",
+    ["2022", "2022-03", "2022-04-21", "2022-05-23 14:34"],
+)
+def test_nested_slice_whole(where: str, freq: str):
+    """Test that slicing splits the pfl in 2 non-overlapping pieces without gap
+    (i.e., ensure that each original timestamp is in exactly one of the resulting pieces.)
+    """
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfl1 = dev.get_nestedpfline(index)
+    left, right = pfl1.slice[:where], pfl1.slice[where:]
+    # Test that each timestamp is present at least once.
+    pd.testing.assert_index_equal(left.index.union(right.index), index)
+    # Test that no timestamp is present twice.
+    assert len(left.index.intersection(right.index)) == 0
diff --git a/tests/core/pfstate/test_slice_state.py b/tests/core/pfstate/test_slice_state.py
new file mode 100644
index 0000000..6a5a960
--- /dev/null
+++ b/tests/core/pfstate/test_slice_state.py
@@ -0,0 +1,68 @@
+"""Test if slice attributes works properly with portfolio state."""
+
+import pytest
+import pandas as pd
+from portfolyo import dev
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize("slice_start", ["2021", "2022", "2022-01-02"])
+@pytest.mark.parametrize(
+    "slice_end",
+    [
+        # (<param for slice>, <param for loc>)
+        ("2021", "2020"),
+        ("2022", "2021"),
+        ("2022-01-02", "2022-01-01"),
+    ],
+)
+def test_slice_state(slice_start, slice_end, freq):
+    index = pd.date_range("2020", "2024", freq=freq)
+    pfs = dev.get_pfstate(index)
+
+    pfs_to_concat = [pfs.slice[: slice_end[0]], pfs.slice[slice_start:]]
+    pfs_to_concat2 = [pfs.loc[: slice_end[1]], pfs.loc[slice_start:]]
+    assert pfs_to_concat == pfs_to_concat2
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize("slice_start", ["2021", "2022", "2022-01-02"])
+def test_state_slice_start(slice_start, freq):
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfs = dev.get_pfstate(index)
+    assert pfs.slice[slice_start:] == pfs.loc[slice_start:]
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize(
+    "slice_end",
+    [
+        # (<param for slice>, <param for loc>)
+        ("2021", "2020"),
+        ("2022", "2021"),
+        ("2021-07", "2021-06"),
+        ("2022-01-02", "2022-01-01"),
+    ],
+)
+def test_state_slice_end(slice_end, freq):
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfs = dev.get_pfstate(index)
+    assert pfs.slice[: slice_end[0]] == pfs.loc[: slice_end[1]]
+
+
+@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D", "15T"])
+@pytest.mark.parametrize(
+    "where",
+    ["2022", "2022-03", "2022-04-21", "2022-05-23 14:34"],
+)
+def test_state_slice_whole(where: str, freq: str):
+    """Test that slicing splits the pfl in 2 non-overlapping pieces without gap
+    (i.e., ensure that each original timestamp is in exactly one of the resulting pieces.)
+    """
+    index = pd.date_range("2020", "2024", freq=freq, inclusive="left")
+    pfs = dev.get_pfstate(index)
+    left, right = pfs.slice[:where], pfs.slice[where:]
+    # Test that each timestamp is present at least once.
+    pd.testing.assert_index_equal(left.index.union(right.index), index)
+    # Test that no timestamp is present twice.
+    assert len(left.index.intersection(right.index)) == 0