all changes with black

Author: erfannariman

doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst

@@ -122,10 +122,9 @@ aggregating statistics for given columns can be defined using the
 
 .. ipython:: python
 
-    titanic.agg({
-        "Age": ["min", "max", "median", "skew"],
-        "Fare": ["min", "max", "median", "mean"]
-    })
+    titanic.agg(
+        {"Age": ["min", "max", "median", "skew"], "Fare": ["min", "max", "median", "mean"]}
+    )
 
 .. raw:: html
 

doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst

@@ -102,9 +102,7 @@ measurement.
 .. ipython:: python
 
     air_quality = pd.read_csv(
-        "data/air_quality_long.csv",
-        index_col="date.utc",
-        parse_dates=True
+        "data/air_quality_long.csv", index_col="date.utc", parse_dates=True
     )
     air_quality.head()
 
@@ -251,9 +249,8 @@ I want the mean concentrations for :math:`NO_2` and :math:`PM_{2.5}` in each of
 .. ipython:: python
 
     air_quality.pivot_table(
-        values="value", index="location",
-        columns="parameter", aggfunc="mean"
-)
+        values="value", index="location", columns="parameter", aggfunc="mean"
+    )
 
 In the case of :meth:`~DataFrame.pivot`, the data is only rearranged. When multiple
 values need to be aggregated (in this specific case, the values on
@@ -356,13 +353,9 @@ The :func:`pandas.melt` method can be defined in more detail:
 
     no_2 = no2_pivoted.melt(
         id_vars="date.utc",
-        value_vars=[
-            "BETR801",
-            "FR04014",
-            "London Westminster"
-        ],
+        value_vars=["BETR801", "FR04014", "London Westminster"],
         value_name="NO_2",
-        var_name="id_location"
+        var_name="id_location",
     )
     no_2.head()
 

doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst

@@ -155,10 +155,7 @@ index. For example:
 
 .. ipython:: python
 
-    air_quality_ = pd.concat([
-        air_quality_pm25, air_quality_no2],
-        keys=["PM25", "NO2"
-    ])
+    air_quality_ = pd.concat([air_quality_pm25, air_quality_no2], keys=["PM25", "NO2"])
 
 .. ipython:: python
 
@@ -235,12 +232,7 @@ Add the station coordinates, provided by the stations metadata table, to the cor
 
 .. ipython:: python
 
-    air_quality = pd.merge(
-        air_quality,
-        stations_coord,
-        how='left',
-        on='location'
-    )
+    air_quality = pd.merge(air_quality, stations_coord, how="left", on="location")
     air_quality.head()
 
 Using the :meth:`~pandas.merge` function, for each of the rows in the

doc/source/getting_started/intro_tutorials/10_text_data.rst

@@ -224,9 +224,7 @@ In the "Sex" column, replace values of "male" by "M" and values of "female" by "
 
 .. ipython:: python
 
-    titanic["Sex_short"] = titanic["Sex"].replace({
-        "male": "M", "female": "F"
-    })
+    titanic["Sex_short"] = titanic["Sex"].replace({"male": "M", "female": "F"})
     titanic["Sex_short"]
 
 Whereas :meth:`~Series.replace` is not a string method, it provides a convenient way