Replace deprecated pandas inplace DataFrame calls

yfinance/base.py

@@ -739,7 +739,7 @@ def _get_earnings_dates_using_scrape(self, limit = 12, offset = 0) -> Optional[p
             df = df.drop(["Symbol", "Company"], axis=1)
 
             # Backwards compatibility
-            df.rename(columns={'Surprise (%)': 'Surprise(%)'}, inplace=True)
+            df = df.rename(columns={'Surprise (%)': 'Surprise(%)'})
 
             df = df.dropna(subset="Earnings Date")
 
@@ -825,9 +825,9 @@ def _get_earnings_dates_using_screener(self, limit=12) -> Optional[pd.DataFrame]
         df[columns_to_update] = df[columns_to_update].astype('float64').replace(0.0, np.nan)
 
         # Format the dataframe
-        df.drop(['Event Start Date', 'Timezone short name'], axis=1, inplace=True)
-        df.set_index('Earnings Date', inplace=True)
-        df.rename(columns={'Surprise (%)': 'Surprise(%)'}, inplace=True)  # Compatibility
+        df = df.drop(['Event Start Date', 'Timezone short name'], axis=1)
+        df = df.set_index('Earnings Date')
+        df = df.rename(columns={'Surprise (%)': 'Surprise(%)'})  # Compatibility
 
         self._earnings_dates[limit] = df
         return df

yfinance/calendars.py

@@ -282,9 +282,9 @@ def _cleanup_df(self, calendar_type: str, df: pd.DataFrame) -> pd.DataFrame:
             df[nan_cols] = df[nan_cols].astype("float64").replace(0.0, np.nan)
 
         # Format the dataframe
-        df.set_index(predef_cal["df_index"], inplace=True)
+        df = df.set_index(predef_cal["df_index"])
         for rename_from, rename_to in predef_cal["renames"].items():
-            df.rename(columns={rename_from: rename_to}, inplace=True)
+            df = df.rename(columns={rename_from: rename_to})
 
         for datetime_col in predef_cal["datetime_cols"]:
             df[datetime_col] = pd.to_datetime(df[datetime_col])

yfinance/multi.py

@@ -218,11 +218,11 @@ def _download_impl(ctx, tickers, start=None, end=None, actions=False, threads=Tr
     except Exception:
         data = _pd.concat(ctx.dfs.values(), axis=1, sort=True,
                           keys=ctx.dfs.keys(), names=['Ticker', 'Price'])
-    data.rename(columns=ctx.isins, inplace=True)
+    data = data.rename(columns=ctx.isins)
 
     if group_by == 'column' and isinstance(data.columns, _pd.MultiIndex):
         data.columns = data.columns.swaplevel(0, 1)
-        data.sort_index(level=0, axis=1, inplace=True)
+        data = data.sort_index(level=0, axis=1)
 
     if not multi_level_index and len(tickers) == 1:
         data = data.droplevel(0 if group_by == 'ticker' else 1, axis=1).rename_axis(None, axis=1)

yfinance/scrapers/analysis.py

@@ -173,7 +173,7 @@ def _fetch_earnings_history(self, methodology) -> pd.DataFrame:
         df = pd.DataFrame(rows)
         if 'quarter' in df.columns:
             df['quarter'] = pd.to_datetime(df['quarter'], format='%Y-%m-%d')
-            df.set_index('quarter', inplace=True)
+            df = df.set_index('quarter')
 
         label = _eps_methodology_label(methodology, module_data)
         if label is not None:

yfinance/scrapers/holders.py

@@ -116,7 +116,7 @@ def _parse_institution_ownership(self, data):
         df = pd.DataFrame(holders)
         if not df.empty:
             df["reportDate"] = pd.to_datetime(df["reportDate"], unit="s")
-            df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "position": "Shares", "value": "Value"}, inplace=True)  # "pctHeld": "% Out"
+            df = df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "position": "Shares", "value": "Value"})  # "pctHeld": "% Out"
         self._institutional = df
 
     def _parse_fund_ownership(self, data):
@@ -128,7 +128,7 @@ def _parse_fund_ownership(self, data):
         df = pd.DataFrame(holders)
         if not df.empty:
             df["reportDate"] = pd.to_datetime(df["reportDate"], unit="s")
-            df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "position": "Shares", "value": "Value"}, inplace=True)
+            df = df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "position": "Shares", "value": "Value"})
         self._mutualfund = df
 
     def _parse_major_direct_holders(self, data):
@@ -140,7 +140,7 @@ def _parse_major_direct_holders(self, data):
         df = pd.DataFrame(holders)
         if not df.empty:
             df["reportDate"] = pd.to_datetime(df["reportDate"], unit="s")
-            df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "positionDirect": "Shares", "valueDirect": "Value"}, inplace=True)
+            df = df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "positionDirect": "Shares", "valueDirect": "Value"})
         self._major_direct_holders = df
 
     def _parse_major_holders_breakdown(self, data):
@@ -149,7 +149,7 @@ def _parse_major_holders_breakdown(self, data):
         df = pd.DataFrame.from_dict(data, orient="index")
         if not df.empty:
             df.columns.name = "Breakdown"
-            df.rename(columns={df.columns[0]: 'Value'}, inplace=True)
+            df = df.rename(columns={df.columns[0]: 'Value'})
         self._major = df
 
     def _parse_insider_transactions(self, data):
@@ -161,7 +161,7 @@ def _parse_insider_transactions(self, data):
         df = pd.DataFrame(holders)
         if not df.empty:
             df["startDate"] = pd.to_datetime(df["startDate"], unit="s")
-            df.rename(columns={
+            df = df.rename(columns={
                 "startDate": "Start Date",
                 "filerName": "Insider",
                 "filerRelation": "Position",
@@ -171,7 +171,7 @@ def _parse_insider_transactions(self, data):
                 "shares": "Shares",
                 "value": "Value",
                 "ownership": "Ownership"  # ownership flag, direct or institutional
-            }, inplace=True)
+            })
         self._insider_transactions = df
 
     def _parse_insider_holders(self, data):
@@ -187,7 +187,7 @@ def _parse_insider_holders(self, data):
             if "latestTransDate" in df:
                 df["latestTransDate"] = pd.to_datetime(df["latestTransDate"], unit="s")
 
-            df.rename(columns={
+            df = df.rename(columns={
                 "name": "Name",
                 "relation": "Position",
                 "url": "URL",
@@ -197,7 +197,7 @@ def _parse_insider_holders(self, data):
                 "positionDirect": "Shares Owned Directly",
                 "positionIndirectDate": "Position Indirect Date",
                 "positionIndirect": "Shares Owned Indirectly"
-            }, inplace=True)
+            })
 
             df["Name"] = df["Name"].astype(str)
             df["Position"] = df["Position"].astype(str)

yfinance/scrapers/quote.py

@@ -572,8 +572,8 @@ def upgrades_downgrades(self) -> pd.DataFrame:
                     if len(data) == 0:
                         raise YFDataException(f"No upgrade/downgrade history found for {self._symbol}")
                     df = pd.DataFrame(data)
-                    df.rename(columns={"epochGradeDate": "GradeDate", 'firm': 'Firm', 'toGrade': 'ToGrade', 'fromGrade': 'FromGrade', 'action': 'Action'}, inplace=True)
-                    df.set_index('GradeDate', inplace=True)
+                    df = df.rename(columns={"epochGradeDate": "GradeDate", 'firm': 'Firm', 'toGrade': 'ToGrade', 'fromGrade': 'FromGrade', 'action': 'Action'})
+                    df = df.set_index('GradeDate')
                     df.index = pd.to_datetime(df.index, unit='s')
                     self._upgrades_downgrades = df
                 except (KeyError, IndexError):

yfinance/tickers.py

@@ -85,7 +85,7 @@ def download(self, period='1mo if start & end None', interval="1d",
 
         if group_by == 'column':
             data.columns = data.columns.swaplevel(0, 1)
-            data.sort_index(level=0, axis=1, inplace=True)
+            data = data.sort_index(level=0, axis=1)
 
         return data
 

yfinance/utils.py

@@ -511,14 +511,14 @@ def auto_adjust(data):
     df["Adj High"] = df["High"] * ratio
     df["Adj Low"] = df["Low"] * ratio
 
-    df.drop(
+    df = df.drop(
         ["Open", "High", "Low", "Close"],
-        axis=1, inplace=True)
+        axis=1)
 
-    df.rename(columns={
+    df = df.rename(columns={
         "Adj Open": "Open", "Adj High": "High",
         "Adj Low": "Low", "Adj Close": "Close"
-    }, inplace=True)
+    })
 
     return df[[c for c in col_order if c in df.columns]]
 
@@ -533,14 +533,14 @@ def back_adjust(data):
     df["Adj High"] = df["High"] * ratio
     df["Adj Low"] = df["Low"] * ratio
 
-    df.drop(
+    df = df.drop(
         ["Open", "High", "Low", "Adj Close"],
-        axis=1, inplace=True)
+        axis=1)
 
-    df.rename(columns={
+    df = df.rename(columns={
         "Adj Open": "Open", "Adj High": "High",
         "Adj Low": "Low"
-    }, inplace=True)
+    })
 
     return df[[c for c in col_order if c in df.columns]]
 
@@ -565,7 +565,7 @@ def parse_quotes(data):
                             "Adj Close": adjclose,
                             "Volume": volumes})
     quotes.index = _pd.to_datetime(timestamps, unit="s")
-    quotes.sort_index(inplace=True)
+    quotes = quotes.sort_index()
     for c in ['Open', 'High', 'Low', 'Close', 'Adj Close']:
         if not is_float_dtype(quotes[c].dtype):
             # Only seen when Adj Close contains Infinity.
@@ -582,9 +582,9 @@ def parse_actions(data):
         if "dividends" in data["events"] and len(data["events"]['dividends']) > 0:
             dividends = _pd.DataFrame(
                 data=list(data["events"]["dividends"].values()))
-            dividends.set_index("date", inplace=True)
+            dividends = dividends.set_index("date")
             dividends.index = _pd.to_datetime(dividends.index, unit="s")
-            dividends.sort_index(inplace=True)
+            dividends = dividends.sort_index()
             if 'currency' in dividends.columns and (dividends['currency'] == '').all():
                 # Currency column useless, drop it.
                 dividends = dividends.drop('currency', axis=1)
@@ -593,17 +593,17 @@ def parse_actions(data):
         if "capitalGains" in data["events"] and len(data["events"]['capitalGains']) > 0:
             capital_gains = _pd.DataFrame(
                 data=list(data["events"]["capitalGains"].values()))
-            capital_gains.set_index("date", inplace=True)
+            capital_gains = capital_gains.set_index("date")
             capital_gains.index = _pd.to_datetime(capital_gains.index, unit="s")
-            capital_gains.sort_index(inplace=True)
+            capital_gains = capital_gains.sort_index()
             capital_gains.columns = ["Capital Gains"]
 
         if "splits" in data["events"] and len(data["events"]['splits']) > 0:
             splits = _pd.DataFrame(
                 data=list(data["events"]["splits"].values()))
-            splits.set_index("date", inplace=True)
+            splits = splits.set_index("date")
             splits.index = _pd.to_datetime(splits.index, unit="s")
-            splits.sort_index(inplace=True)
+            splits = splits.sort_index()
             splits["Stock Splits"] = splits["numerator"] / splits["denominator"]
             splits = splits[["Stock Splits"]]