4 drop now also accepts index/columns keywords . . . . . . . . . . . . . . . . . . . 10 1.2.1.5 rename, reindex now also accept axis keyword . . . . . . . . . . . . . . . . . 10 1.2.1.6 CategoricalDtype objects now have a pipe method . . . . . . . . . . . . . . . . . . . . . 12 1.2.1.8 Categorical.rename_categories accepts a dict-like . . . . . . . . . . . . 13 1.2.1.9 Other Enhancements . . . . . functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . 140 1.10.1.2 Changes to rename . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 1.10.1.3 Range Index .

functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Changes to rename . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Range Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1282 pandas.Series.cat.rename_categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1282 pandas.Series.cat.reorder_categories reindex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1579 pandas.Index.rename . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1580 pandas.Index.repeat

functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Changes to rename . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Range Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285 pandas.Series.cat.rename_categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285 pandas.Series.cat.reorder_categories reindex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1583 pandas.Index.rename . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1583 pandas.Index.repeat

3]] Length: 2, closed: right, dtype: interval[int64] 1.5.3 DataFrame.rename now only accepts one positional argument DataFrame.rename() would previously accept positional arguments that would lead to ambiguous df.rename({0: 1}, {0: 2}) FutureWarning: ...Use named arguments to resolve ambiguity... 2 1 1 pandas 1.0.0 >>> df.rename({0: 1}, {0: 2}) Traceback (most recent call last): ... TypeError: rename() takes arguments are provided. pandas 0.25.x >>> df.rename({0: 1}, index={0: 2}) 0 1 1 >>> df.rename(mapper={0: 1}, index={0: 2}) 0 2 1 pandas 1.0.0 >>> df.rename({0: 1}, index={0: 2}) Traceback (most recent

conditional expression. I want to rename the data columns to the corresponding station identifiers used by openAQ In [8]: air_quality_renamed = air_quality.rename( ...: columns={"station_antwerp": "BETR801" 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 ˓→ NaN 2019-05-07 06:00:00 NaN 61.9 NaN NaN ˓→ NaN The rename() function can be used for both row labels and column labels. Provide a dictionary with the keys letters can be done using a function as well: In [10]: air_quality_renamed = air_quality_renamed.rename(columns=str.lower) In [11]: air_quality_renamed.head() Out[11]: betr801 fr04014 london westminster

conditional expression. I want to rename the data columns to the corresponding station identifiers used by openAQ In [8]: air_quality_renamed = air_quality.rename( ...: columns={"station_antwerp": "BETR801" 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 ˓→ NaN 2019-05-07 06:00:00 NaN 61.9 NaN NaN ˓→ NaN The rename() function can be used for both row labels and column labels. Provide a dictionary with the keys letters can be done using a function as well: In [10]: air_quality_renamed = air_quality_renamed.rename(columns=str.lower) In [11]: air_quality_renamed.head() Out[11]: betr801 fr04014 london westminster

Python code via apply(). I want to rename the data columns to the corresponding station identifiers used by openAQ In [8]: air_quality_renamed = air_quality.rename( ...: columns={ ...: "station_antwerp": 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 ˓→ NaN 2019-05-07 06:00:00 NaN 61.9 NaN NaN ˓→ NaN The rename() function can be used for both row labels and column labels. Provide a dictionary with the keys powerful Python data analysis toolkit, Release 1.3.2 In [10]: air_quality_renamed = air_quality_renamed.rename(columns=str.lower) In [11]: air_quality_renamed.head() Out[11]: betr801 fr04014 london westminster

Python code via apply(). I want to rename the data columns to the corresponding station identifiers used by openAQ In [8]: air_quality_renamed = air_quality.rename( ...: columns={ ...: "station_antwerp": 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 ␣ ˓→ NaN 2019-05-07 06:00:00 NaN 61.9 NaN NaN ␣ ˓→ NaN The rename() function can be used for both row labels and column labels. Provide a dictionary with the keys letters can be done using a function as well: In [10]: air_quality_renamed = air_quality_renamed.rename(columns=str.lower) In [11]: air_quality_renamed.head() Out[11]: betr801 fr04014 london westminster

Python code via apply(). I want to rename the data columns to the corresponding station identifiers used by openAQ In [8]: air_quality_renamed = air_quality.rename( ...: columns={ ...: "station_antwerp": 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 ␣ ˓→ NaN 2019-05-07 06:00:00 NaN 61.9 NaN NaN ␣ ˓→ NaN The rename() function can be used for both row labels and column labels. Provide a dictionary with the keys letters can be done using a function as well: In [10]: air_quality_renamed = air_quality_renamed.rename(columns=str.lower) In [11]: air_quality_renamed.head() Out[11]: betr801 fr04014 london westminster

functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . 111 1.8.1.2 Changes to rename . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 1.8.1.3 Range Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1401 34.3.15.4 pandas.Series.cat.rename_categories . . . . . . . . . . . . . . . . . . . . . . . . . 1402 34.3.15.5 pandas.Series.cat.reorder_categories reindex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1653 34.6.1.86 pandas.Index.rename . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1653 34.6.1.87 pandas.Index.repeat