Reduce Pandas deprecation warnings.

src/zipline/_protocol.pyx

@@ -659,7 +659,7 @@ cdef class BarData:
             df = (pd.concat(df_dict,
                             keys=df_dict.keys(),
                             names=['fields', dt_label])
-                  .stack(dropna=False)  # ensure we return all fields/assets/dates despite missing values
+                  .stack(future_stack=True)  # ensure we return all fields/assets/dates despite missing values
                   .unstack(level='fields'))
             df.index.set_names([dt_label, 'asset'])
             return df.sort_index()

src/zipline/assets/asset_writer.py

@@ -319,7 +319,7 @@ def check_intersections(persymbol):
             ambiguous[persymbol.name] = intersections, msg_component
 
     mappings.groupby(["symbol", "country_code"], group_keys=False).apply(
-        check_intersections
+        check_intersections, include_groups=False
     )
 
     if ambiguous:

src/zipline/data/bcolz_minute_bars.py

@@ -964,8 +964,8 @@ def _minutes_to_exclude(self):
         market_closes = self._market_closes.values.astype("datetime64[m]")
         minutes_per_day = (market_closes - market_opens).astype(np.int64)
         early_indices = np.where(minutes_per_day != self._minutes_per_day - 1)[0]
-        early_opens = self._market_opens[early_indices]
-        early_closes = self._market_closes[early_indices]
+        early_opens = self._market_opens.iloc[early_indices]
+        early_closes = self._market_closes.iloc[early_indices]
         minutes = [
             (market_open, early_close)
             for market_open, early_close in zip(early_opens, early_closes)

src/zipline/data/bundles/quandl.py

@@ -103,7 +103,7 @@ def gen_asset_metadata(data, show_progress):
     if show_progress:
         log.info("Generating asset metadata.")
 
-    data = data.groupby(by="symbol").agg({"date": [np.min, np.max]})
+    data = data.groupby(by="symbol").agg({"date": ["min", "max"]})
     data.reset_index(inplace=True)
     data["start_date"] = data.date[np.min.__name__]
     data["end_date"] = data.date[np.max.__name__]

src/zipline/data/data_portal.py

@@ -348,7 +348,9 @@ def handle_extra_source(self, source_df, sim_params):
         group_names = grouped_by_sid.groups.keys()
         group_dict = {}
         for group_name in group_names:
-            group_dict[group_name] = grouped_by_sid.get_group(group_name)
+            # FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas.
+            # Pass `(name,)` instead of `name` to silence this warning.
+            group_dict[group_name] = grouped_by_sid.get_group((group_name,))
 
         # This will be the dataframe which we query to get fetcher assets at
         # any given time. Get's overwritten every time there's a new fetcher
@@ -948,7 +950,7 @@ def get_history_window(
             df.iloc[0, assets_with_leading_nan] = np.array(
                 initial_values, dtype=np.float64
             )
-            df.fillna(method="ffill", inplace=True)
+            df.ffill(inplace=True)
 
             # forward-filling will incorrectly produce values after the end of
             # an asset's lifetime, so write NaNs back over the asset's

src/zipline/finance/ledger.py

@@ -417,7 +417,8 @@ def end_of_bar(self, session_ix):
         # make daily_returns hold the partial returns, this saves many
         # metrics from doing a concat and copying all of the previous
         # returns
-        self.daily_returns_array.iloc[session_ix] = self.todays_returns
+        # AttributeError: 'numpy.ndarray' object has no attribute 'iloc'
+        self.daily_returns_array[session_ix] = self.todays_returns
 
     def end_of_session(self, session_ix):
         # save the daily returns time-series

src/zipline/finance/metrics/metric.py

@@ -565,7 +565,8 @@ def risk_report(cls, algorithm_returns, benchmark_returns, algorithm_leverages):
             start=start_session,
             # Ensure we have at least one month
             end=end - datetime.timedelta(days=1),
-            freq="M",
+            # FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
+            freq="ME",
             tz="utc",
         )
 

src/zipline/finance/slippage.py

@@ -506,7 +506,7 @@ def _get_window_data(self, data, asset, window_length):
             # always just NaN.
             close_volatility = (
                 close_history[:-1]
-                .pct_change()[1:]
+                .pct_change(fill_method=None)[1:]
                 .std(
                     skipna=False,
                 )

src/zipline/pipeline/loaders/earnings_estimates.py

@@ -702,7 +702,7 @@ def get_last_data_per_qtr(
         ffill_across_cols(last_per_qtr, columns, self.name_map)
         # Stack quarter and sid into the index.
         stacked_last_per_qtr = last_per_qtr.stack(
-            [SID_FIELD_NAME, NORMALIZED_QUARTERS],
+            [SID_FIELD_NAME, NORMALIZED_QUARTERS], future_stack=True
         )
         # Set date index name for ease of reference
         stacked_last_per_qtr.index.set_names(

src/zipline/sources/benchmark_source.py

@@ -292,7 +292,7 @@ def _initialize_precalculated_series(
             first_day_return = (first_close - first_open) / first_open
 
             returns = benchmark_series.pct_change()[:]
-            returns[0] = first_day_return
+            returns.iloc[0] = first_day_return
             return returns, returns
         else:
             raise ValueError(

src/zipline/sources/requests_csv.py

@@ -410,7 +410,7 @@ def __iter__(self):
             # the dt column is dropped. So, we need to manually copy
             # dt into the event.
             event.dt = dt
-            for k, v in series.iteritems():
+            for k, v in series.items():
                 # convert numpy integer types to
                 # int. This assumes we are on a 64bit
                 # platform that will not lose information

src/zipline/testing/core.py

@@ -525,11 +525,11 @@ def create_daily_df_for_asset(trading_calendar, start_day, end_day, interval=1):
         # only keep every 'interval' rows
         for idx, _ in enumerate(days_arr):
             if (idx + 1) % interval != 0:
-                df["open"].iloc[idx] = 0
-                df["high"].iloc[idx] = 0
-                df["low"].iloc[idx] = 0
-                df["close"].iloc[idx] = 0
-                df["volume"].iloc[idx] = 0
+                df.loc[idx, "open"] = 0
+                df.loc[idx, "high"] = 0
+                df.loc[idx, "low"] = 0
+                df.loc[idx, "close"] = 0
+                df.loc[idx, "volume"] = 0
 
     return df
 
@@ -1287,13 +1287,13 @@ def patch_os_environment(remove=None, **values):
     remove = remove or []
     for key in remove:
         old_values[key] = os.environ.pop(key)
-    for key, value in values.iteritems():
+    for key, value in values.items():
         old_values[key] = os.getenv(key)
         os.environ[key] = value
     try:
         yield
     finally:
-        for old_key, old_value in old_values.iteritems():
+        for old_key, old_value in old_values.items():
             if old_value is None:
                 # Value was not present when we entered, so del it out if it's
                 # still present.

src/zipline/utils/events.py

@@ -483,7 +483,7 @@ def execution_period_values(self):
             # Group by ISO year (0) and week (1)
             .groupby(sessions.map(lambda x: x.isocalendar()[0:2]))
             .nth(self.td_delta)
-            .view(np.int64)
+            .astype(np.int64)
         )
 
 

tests/data/test_resample.py

@@ -787,7 +787,7 @@ def test_load_raw_arrays(self):
         # The value is the autogenerated value from test fixtures.
         assert_almost_equal(
             10.0,
-            opens[1][first_minute_loc],
+            opens.iloc[first_minute_loc][1],
             err_msg="The value for Equity 1, should be 10.0, at NYSE open.",
         )
 
@@ -850,7 +850,7 @@ def test_load_raw_arrays(self):
 
         assert_almost_equal(
             nan,
-            opens[1][tday_loc],
+            opens.iloc[tday_loc][1],
             err_msg="2015-11-26 should be `nan`, since Thanksgiving is a "
             "holiday in the reader's calendar.",
         )
@@ -861,7 +861,7 @@ def test_load_raw_arrays(self):
 
         assert_almost_equal(
             nan,
-            opens[1][tday_loc],
+            opens.iloc[tday_loc][1],
             err_msg="2015-11-26 should be `nan`, since Thanksgiving is a "
             "holiday in the reader's calendar.",
         )

tests/finance/test_commissions.py

@@ -441,8 +441,8 @@ def test_futures_per_trade(self):
         # The capital used is only -1.0 (the commission cost) because no
         # capital is actually spent to enter into a long position on a futures
         # contract.
-        assert results.orders[1][0]["commission"] == 1.0
-        assert results.capital_used[1] == -1.0
+        assert results.orders.iloc[1][0]["commission"] == 1.0
+        assert results.capital_used.iloc[1] == -1.0
 
     def test_per_share_no_minimum(self):
         results = self.get_results(
@@ -487,9 +487,9 @@ def test_per_share_with_minimum(self):
         )
 
         # commissions should be 8, 10, 15
-        assert 8 == results.orders[1][0]["commission"]
-        assert 10 == results.orders[2][0]["commission"]
-        assert 15 == results.orders[3][0]["commission"]
+        assert 8 == results.orders.iloc[1][0]["commission"]
+        assert 10 == results.orders.iloc[2][0]["commission"]
+        assert 15 == results.orders.iloc[3][0]["commission"]
 
         self.verify_capital_used(results, [-1008, -1002, -1005])
 
@@ -503,9 +503,9 @@ def test_per_share_with_minimum(self):
         )
 
         # commissions should be 12, 12, 15
-        assert 12 == results.orders[1][0]["commission"]
-        assert 12 == results.orders[2][0]["commission"]
-        assert 15 == results.orders[3][0]["commission"]
+        assert 12 == results.orders.iloc[1][0]["commission"]
+        assert 12 == results.orders.iloc[2][0]["commission"]
+        assert 15 == results.orders.iloc[3][0]["commission"]
 
         self.verify_capital_used(results, [-1012, -1000, -1003])
 
@@ -519,9 +519,9 @@ def test_per_share_with_minimum(self):
         )
 
         # commissions should be 18, 18, 18
-        assert 18 == results.orders[1][0]["commission"]
-        assert 18 == results.orders[2][0]["commission"]
-        assert 18 == results.orders[3][0]["commission"]
+        assert 18 == results.orders.iloc[1][0]["commission"]
+        assert 18 == results.orders.iloc[2][0]["commission"]
+        assert 18 == results.orders.iloc[3][0]["commission"]
 
         self.verify_capital_used(results, [-1018, -1000, -1000])
 
@@ -549,8 +549,8 @@ def test_per_contract(self, min_trade_cost, expected_commission):
             ),
         )
 
-        assert results.orders[1][0]["commission"] == expected_commission
-        assert results.capital_used[1] == -expected_commission
+        assert results.orders.iloc[1][0]["commission"] == expected_commission
+        assert results.capital_used.iloc[1] == -expected_commission
 
     def test_per_dollar(self):
         results = self.get_results(
@@ -583,6 +583,7 @@ def test_incorrectly_set_futures_model(self):
             )
 
     def verify_capital_used(self, results, values):
-        assert values[0] == results.capital_used[1]
-        assert values[1] == results.capital_used[2]
-        assert values[2] == results.capital_used[3]
+        # assert values[0] == results.capital_used.iloc[1]
+        # assert values[1] == results.capital_used.iloc[2]
+        # assert values[2] == results.capital_used.iloc[3]
+        assert values == results.capital_used[1:4].tolist()

tests/metrics/test_metrics.py

@@ -138,7 +138,7 @@ def test_nop(self):
             )
 
         nan_then_zero = pd.Series(0.0, index=self.closes)
-        nan_then_zero[0] = float("nan")
+        nan_then_zero.iloc[0] = float("nan")
         nan_then_zero_fields = (
             "algo_volatility",
             "benchmark_volatility",
@@ -667,7 +667,7 @@ def handle_data(context, data):
                 )
 
         nan_then_zero = pd.Series(0.0, index=self.closes)
-        nan_then_zero[0] = float("nan")
+        nan_then_zero.iloc[0] = float("nan")
         nan_then_zero_fields = (
             "algo_volatility",
             "benchmark_volatility",
@@ -727,7 +727,7 @@ def handle_data(context, data):
             check_names=False,
         )
 
-        expected_cash[0] += cash_modifier
+        expected_cash.iloc[0] += cash_modifier
         assert_equal(
             perf["ending_cash"],
             expected_cash,
@@ -736,7 +736,7 @@ def handle_data(context, data):
 
         # we purchased one share on the first day
         expected_capital_used = pd.Series(0.0, index=self.closes)
-        expected_capital_used[0] += cash_modifier
+        expected_capital_used.iloc[0] += cash_modifier
 
         assert_equal(
             perf["capital_used"],
@@ -760,7 +760,7 @@ def handle_data(context, data):
 
         # we don't start with any positions; the first day has no starting
         # exposure
-        expected_position_exposure[0] = 0
+        expected_position_exposure.iloc[0] = 0
         for field in "starting_value", "starting_exposure":
             # for equities, position value and position exposure are the same
             assert_equal(
@@ -883,8 +883,8 @@ def handle_data(context, data):
             cash_modifier,
             index=self.trading_minutes,
         )
-        expected_portfolio_capital_used[0] = 0.0
-        expected_capital_used[0] = 0
+        expected_portfolio_capital_used.iloc[0] = 0.0
+        expected_capital_used.iloc[0] = 0
         assert_equal(
             portfolio_snapshots["cash_flow"],
             expected_portfolio_capital_used,
@@ -1670,15 +1670,15 @@ def handle_data(context, data):
             # we sold one share on the first day
             cash_modifier = +expected_fill_price
 
-        expected_cash[1:] += cash_modifier
+        expected_cash.iloc[1:] += cash_modifier
 
         assert_equal(
             perf["starting_cash"],
             expected_cash,
             check_names=False,
         )
 
-        expected_cash[0] += cash_modifier
+        expected_cash.iloc[0] += cash_modifier
         assert_equal(
             perf["ending_cash"],
             expected_cash,
@@ -1687,7 +1687,7 @@ def handle_data(context, data):
 
         # we purchased one share on the first day
         expected_capital_used = pd.Series(0.0, index=self.equity_closes)
-        expected_capital_used[0] += cash_modifier
+        expected_capital_used.iloc[0] += cash_modifier
 
         assert_equal(
             perf["capital_used"],
@@ -1707,7 +1707,7 @@ def handle_data(context, data):
         # we don't start with any positions; the first day has no starting
         # exposure
         expected_starting_exposure = expected_exposure.shift(1)
-        expected_starting_exposure[0] = 0.0
+        expected_starting_exposure.iloc[0] = 0.0
         for field in "starting_value", "starting_exposure":
             # for equities, position value and position exposure are the same
             assert_equal(
@@ -1815,8 +1815,8 @@ def handle_data(context, data):
             cash_modifier,
             index=self.equity_minutes,
         )
-        expected_portfolio_capital_used[0] = 0.0
-        expected_capital_used[0] = 0
+        expected_portfolio_capital_used.iloc[0] = 0.0
+        expected_capital_used.iloc[0] = 0
         assert_equal(
             portfolio_snapshots["cash_flow"],
             expected_portfolio_capital_used,

tests/pipeline/test_downsampling.py

@@ -637,7 +637,8 @@ def check_downsampled_term(self, term):
 
         expected_results = {
             "year": (
-                raw_term_results.groupby(pd.Grouper(freq="AS"))
+                # FutureWarning: 'AS' is deprecated and will be removed in a future version, please use 'YS' instead.
+                raw_term_results.groupby(pd.Grouper(freq="YS"))
                 .first()
                 .reindex(compute_dates, method="ffill")
             ),

tests/pipeline/test_engine.py

@@ -256,7 +256,7 @@ def test_same_day_pipeline(self):
         #  (i.e. start and end dates are the same) we should accurately get
         # data for the day prior.
         result = self.engine.run_pipeline(p, self.dates[1], self.dates[1])
-        assert result["f"][0] == 1.0
+        assert result["f"].iloc[0] == 1.0
 
     def test_screen(self):
         asset_ids = np.array(self.asset_ids)

tests/pipeline/test_events.py

@@ -477,7 +477,7 @@ def check_previous_value_results(self, column, results, dates):
         # from pandas won't be tz_localized.
         dates = dates.tz_localize(None)
 
-        for asset, asset_result in results.iteritems():
+        for asset, asset_result in results.items():
             relevant_events = events[events.sid == asset.sid]
             assert len(relevant_events) == 2
 
@@ -520,7 +520,7 @@ def check_next_value_results(self, column, results, dates):
         # Remove timezone info from trading days, since the outputs
         # from pandas won't be tz_localized.
         dates = dates.tz_localize(None)
-        for asset, asset_result in results.iteritems():
+        for asset, asset_result in results.items():
             relevant_events = events[events.sid == asset.sid]
             assert len(relevant_events) == 2