22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = ( ...: air_quality["station_paris"] Note: In case you are wondering, pivot_table() is indeed directly linked to groupby(). The same result can be derived by grouping on both parameter and location: air_quality.groupby(["parameter", "location"])

22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = ( ...: air_quality["station_paris"] Note: In case you are wondering, pivot_table() is indeed directly linked to groupby(). The same result can be derived by grouping on both parameter and location: air_quality.groupby(["parameter", "location"])

22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = \ ...: air_quality["station_paris"] Note: In case you are wondering, pivot_table() is indeed directly linked to groupby(). The same result can be derived by grouping on both parameter and location: air_quality.groupby(["parameter", "location"])

22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = \ ...: air_quality["station_paris"] Note: In case you are wondering, pivot_table() is indeed directly linked to groupby(). The same result can be derived by grouping on both parameter and location: air_quality.groupby(["parameter", "location"])

Visualization Dependency Minimum Version Notes setuptools 38.6.0 Utils for entry points of plotting backend matplotlib 2.2.3 Plotting library Jinja2 2.10 Conditional formatting with DataFrame.style tabulate 22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = ( ...: air_quality["station_paris"]

Visualization Dependency Minimum Version Notes setuptools 38.6.0 Utils for entry points of plotting backend matplotlib 2.2.3 Plotting library Jinja2 2.10 Conditional formatting with DataFrame.style tabulate 22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = ( ...: air_quality["station_paris"]

Visualization Dependency Minimum Version Notes setuptools 38.6.0 Utils for entry points of plotting backend matplotlib 2.2.3 Plotting library Jinja2 2.10 Conditional formatting with DataFrame.style tabulate 22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = ( ...: air_quality["station_paris"]

Reductions can return ``pd.NA`` When performing a reduction such as a sum with skipna=False, the result will now be pd.NA instead of np.nan in presence of missing values (GH30958). pandas 0.25.x >>> to silence this warning. Series([], dtype: float64) 1.5.10 Result dtype inference changes for resample operations The rules for the result dtype in DataFrame.resample() aggregations have changed for pandas would attempt to convert the result back to the original dtype, falling back to the usual inference rules if that was not possible. Now, pandas will only return a result of the original dtype if the scalar

22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = ( ...: air_quality["station_paris"] Note: In case you are wondering, pivot_table() is indeed directly linked to groupby(). The same result can be derived by grouping on both parameter and location: air_quality.groupby(["parameter", "location"])

22 1 35 2 58 Name: Age, dtype: int64 When selecting a single column of a pandas DataFrame, the result is a pandas Series. To select the column, use the column label in between square brackets []. Note: iterate each of the rows! I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column In [6]: air_quality["ratio_paris_antwerp"] = ( ...: air_quality["station_paris"] Note: In case you are wondering, pivot_table() is indeed directly linked to groupby(). The same result can be derived by grouping on both parameter and location: air_quality.groupby(["parameter", "location"])