From fd76d19b610ef166697c67ff1e6ee15710a73f06 Mon Sep 17 00:00:00 2001 From: Kyle Smith Date: Fri, 25 Oct 2024 15:37:18 -0700 Subject: [PATCH 1/3] Added a notebook --- sklearn-example-2.ipynb | 197 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 sklearn-example-2.ipynb diff --git a/sklearn-example-2.ipynb b/sklearn-example-2.ipynb new file mode 100644 index 0000000..72eaa1f --- /dev/null +++ b/sklearn-example-2.ipynb @@ -0,0 +1,197 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introducing Scikit-Learn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copied from https://github.com/jakevdp/PythonDataScienceHandbook" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "iris = sns.load_dataset('iris')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iris['PCA1'] = X_2D[:, 0]\n", + "iris['PCA2'] = X_2D[:, 1]\n", + "sns.lmplot(x=\"PCA1\", y=\"PCA2\", hue='species', data=iris, fit_reg=False);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n", + "This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Unsupervised learning: Dimensionality reduction\n", + "\n", + "We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to effectively visualize points in such a high-dimensional space.\n", + "Instead we'll reduce the dimensions to 2, using an unsupervised method.\n", + "Here, we'll make use of a manifold learning algorithm called *Isomap* (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)), and transform the data to two dimensions:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 2)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.manifold import Isomap\n", + "iso = Isomap(n_components=2)\n", + "iso.fit(digits.data)\n", + "data_projected = iso.transform(digits.data)\n", + "data_projected.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that the projected data is now two-dimensional.\n", + "Let's plot this data to see if we can learn anything from its structure:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n", + " edgecolor='none', alpha=0.5,\n", + " cmap=plt.cm.get_cmap('spectral', 10))\n", + "plt.colorbar(label='digit label', ticks=range(10))\n", + "plt.clim(-0.5, 9.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space.\n", + "Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n", + "On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with \"hats\" on them, which cause them to look similar to fours.\n", + "\n", + "Overall, however, the different groups appear to be fairly well separated in the parameter space: this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data.\n", + "Let's give it a try." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 9a272922d0fa3539b7fe9770ce18950fe1a1a4e9 Mon Sep 17 00:00:00 2001 From: Kyle Smith Date: Fri, 25 Oct 2024 15:44:44 -0700 Subject: [PATCH 2/3] Added util function --- sklearn-example-2.ipynb | 112 ++++++++++++++++++++++++++++++++++++++-- utils.py | 54 +++++++++++++++++++ 2 files changed, 162 insertions(+), 4 deletions(-) create mode 100644 utils.py diff --git a/sklearn-example-2.ipynb b/sklearn-example-2.ipynb index 72eaa1f..4e0d904 100644 --- a/sklearn-example-2.ipynb +++ b/sklearn-example-2.ipynb @@ -16,16 +16,120 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ColumnData TypeNon-Null CountNull CountUnique ValuesNumeric Stats
0sepal_lengthfloat64150035min: 4.30, max: 7.90, mean: 5.84, median: 5.80
1sepal_widthfloat64150023min: 2.00, max: 4.40, mean: 3.06, median: 3.00
2petal_lengthfloat64150043min: 1.00, max: 6.90, mean: 3.76, median: 4.35
3petal_widthfloat64150022min: 0.10, max: 2.50, mean: 1.20, median: 1.30
4speciesobject15003N/A
\n", + "
" + ], + "text/plain": [ + " Column Data Type Non-Null Count Null Count Unique Values \\\n", + "0 sepal_length float64 150 0 35 \n", + "1 sepal_width float64 150 0 23 \n", + "2 petal_length float64 150 0 43 \n", + "3 petal_width float64 150 0 22 \n", + "4 species object 150 0 3 \n", + "\n", + " Numeric Stats \n", + "0 min: 4.30, max: 7.90, mean: 5.84, median: 5.80 \n", + "1 min: 2.00, max: 4.40, mean: 3.06, median: 3.00 \n", + "2 min: 1.00, max: 6.90, mean: 3.76, median: 4.35 \n", + "3 min: 0.10, max: 2.50, mean: 1.20, median: 1.30 \n", + "4 N/A " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import seaborn as sns\n", - "iris = sns.load_dataset('iris')" + "from utils import summarize_dataframe\n", + "\n", + "iris = sns.load_dataset('iris')\n", + "\n", + "summarize_dataframe(iris)" ] }, { @@ -189,7 +293,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.10.0" } }, "nbformat": 4, diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..970c37d --- /dev/null +++ b/utils.py @@ -0,0 +1,54 @@ +import pandas as pd +import numpy as np + + +def summarize_dataframe(df: pd.DataFrame) -> pd.DataFrame: + """ + Generates a summary of a pandas DataFrame. + + This function provides a quick overview of the DataFrame, including: + - Basic statistics for numeric columns + - Unique value counts for categorical columns + - Missing value counts for all columns + + Parameters: + df (pd.DataFrame): The input DataFrame to summarize + + Returns: + pd.DataFrame: A summary DataFrame with statistics for each column + """ + # Initialize lists to store summary information + columns = [] + dtypes = [] + non_null_counts = [] + null_counts = [] + unique_counts = [] + numeric_stats = [] + + for col in df.columns: + columns.append(col) + dtypes.append(str(df[col].dtype)) + non_null_counts.append(df[col].count()) + null_counts.append(df[col].isnull().sum()) + unique_counts.append(df[col].nunique()) + + if np.issubdtype(df[col].dtype, np.number): + numeric_stats.append( + f"min: {df[col].min():.2f}, max: {df[col].max():.2f}, mean: {df[col].mean():.2f}, median: {df[col].median():.2f}" + ) + else: + numeric_stats.append("N/A") + + # Create summary DataFrame + summary_df = pd.DataFrame( + { + "Column": columns, + "Data Type": dtypes, + "Non-Null Count": non_null_counts, + "Null Count": null_counts, + "Unique Values": unique_counts, + "Numeric Stats": numeric_stats, + } + ) + + return summary_df From 7fdf135f37eacff2f72d61c8568c7143866cd5f2 Mon Sep 17 00:00:00 2001 From: Kyle Smith Date: Fri, 25 Oct 2024 15:52:45 -0700 Subject: [PATCH 3/3] Added sample value to summary --- sklearn-example-2.ipynb | 29 +++++++++++++++++++++-------- utils.py | 7 +++++++ 2 files changed, 28 insertions(+), 8 deletions(-) diff --git a/sklearn-example-2.ipynb b/sklearn-example-2.ipynb index 4e0d904..c716f11 100644 --- a/sklearn-example-2.ipynb +++ b/sklearn-example-2.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, @@ -50,6 +50,7 @@ " Null Count\n", " Unique Values\n", " Numeric Stats\n", + " Sample Values\n", " \n", " \n", " \n", @@ -61,6 +62,7 @@ " 0\n", " 35\n", " min: 4.30, max: 7.90, mean: 5.84, median: 5.80\n", + " [5.0, 4.9, 6.9]\n", " \n", " \n", " 1\n", @@ -70,6 +72,7 @@ " 0\n", " 23\n", " min: 2.00, max: 4.40, mean: 3.06, median: 3.00\n", + " [3.6, 3.1, 2.5]\n", " \n", " \n", " 2\n", @@ -79,6 +82,7 @@ " 0\n", " 43\n", " min: 1.00, max: 6.90, mean: 3.76, median: 4.35\n", + " [4.8, 3.9, 1.5]\n", " \n", " \n", " 3\n", @@ -88,6 +92,7 @@ " 0\n", " 22\n", " min: 0.10, max: 2.50, mean: 1.20, median: 1.30\n", + " [0.2, 0.4, 0.3]\n", " \n", " \n", " 4\n", @@ -97,6 +102,7 @@ " 0\n", " 3\n", " N/A\n", + " ['virginica', 'virginica', 'virginica']\n", " \n", " \n", "\n", @@ -110,15 +116,22 @@ "3 petal_width float64 150 0 22 \n", "4 species object 150 0 3 \n", "\n", - " Numeric Stats \n", - "0 min: 4.30, max: 7.90, mean: 5.84, median: 5.80 \n", - "1 min: 2.00, max: 4.40, mean: 3.06, median: 3.00 \n", - "2 min: 1.00, max: 6.90, mean: 3.76, median: 4.35 \n", - "3 min: 0.10, max: 2.50, mean: 1.20, median: 1.30 \n", - "4 N/A " + " Numeric Stats \\\n", + "0 min: 4.30, max: 7.90, mean: 5.84, median: 5.80 \n", + "1 min: 2.00, max: 4.40, mean: 3.06, median: 3.00 \n", + "2 min: 1.00, max: 6.90, mean: 3.76, median: 4.35 \n", + "3 min: 0.10, max: 2.50, mean: 1.20, median: 1.30 \n", + "4 N/A \n", + "\n", + " Sample Values \n", + "0 [5.0, 4.9, 6.9] \n", + "1 [3.6, 3.1, 2.5] \n", + "2 [4.8, 3.9, 1.5] \n", + "3 [0.2, 0.4, 0.3] \n", + "4 ['virginica', 'virginica', 'virginica'] " ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } diff --git a/utils.py b/utils.py index 970c37d..6254400 100644 --- a/utils.py +++ b/utils.py @@ -10,6 +10,7 @@ def summarize_dataframe(df: pd.DataFrame) -> pd.DataFrame: - Basic statistics for numeric columns - Unique value counts for categorical columns - Missing value counts for all columns + - Sample values for each column Parameters: df (pd.DataFrame): The input DataFrame to summarize @@ -24,6 +25,7 @@ def summarize_dataframe(df: pd.DataFrame) -> pd.DataFrame: null_counts = [] unique_counts = [] numeric_stats = [] + sample_values = [] for col in df.columns: columns.append(col) @@ -39,6 +41,10 @@ def summarize_dataframe(df: pd.DataFrame) -> pd.DataFrame: else: numeric_stats.append("N/A") + # Add sample values + sample = df[col].dropna().sample(n=min(3, df[col].count())).tolist() + sample_values.append(str(sample)) + # Create summary DataFrame summary_df = pd.DataFrame( { @@ -48,6 +54,7 @@ def summarize_dataframe(df: pd.DataFrame) -> pd.DataFrame: "Null Count": null_counts, "Unique Values": unique_counts, "Numeric Stats": numeric_stats, + "Sample Values": sample_values, } )