a-pandas-ex-columns-and-index

Some useful Pandas methods for df.index and df.columns

pip install a-pandas-ex-columns-and-index

from a_pandas_ex_columns_and_index import pd_add_index_and_columns

pd_add_index_and_columns()

import pandas as pd

df = pd.read_csv("https://github.com/pandas-dev/pandas/raw/main/doc/data/air_quality_long.csv")

The code above will add some new methods to your df

df.d_swap_2_columns
df.ds_sort_by_str_length
df.d_insert_column_before_another
df.ds_reverse
df.d_add_prefix_to_column_when_regex_match
df.d_add_prefix_to_index_when_regex_match
df.d_filter_df_by_regex_in_index
df.d_filter_df_by_regex_in_columns
df.d_columns_upper
df.d_index_upper
df.d_index_lower
df.d_columns_lower
df.d_make_columns_dot_compatible
df.d_natsort_index
df.d_natort_columns
df.d_natsort_df_by_column
d_rename_index
d_rename_columns

All methods added to pandas have one of this prefixes:

ds_ (for DataFrames and Series)
s_ (only for Series)
d_ (only for DataFrames)

df.d_swap_2_columns

df.columns

Out[3]: 

Index(['city', 'country', 'date.utc', 'location', 'parameter', 'value',

       'unit'],

      dtype='object')

print(df)

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df2=df.d_swap_2_columns('city', 'country')

print(df2.columns)

print(df2)

Index(['country', 'city', 'date.utc', 'location', 'parameter', 'value',

       'unit'],

      dtype='object')

     country       city                   date.utc  ... parameter value   unit

0         BE  Antwerpen  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1         BE  Antwerpen  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2         BE  Antwerpen  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3         BE  Antwerpen  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4         BE  Antwerpen  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

      ...        ...                        ...  ...       ...   ...    ...

5267      GB     London  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268      GB     London  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269      GB     London  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270      GB     London  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271      GB     London  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.ds_sort_by_str_length

df

Out[3]: 

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df2=df.ds_sort_by_str_length('city')

print(df2)

           city country                   date.utc  ... parameter value   unit

2635      Paris      FR  2019-05-15 05:00:00+00:00  ...       no2  46.5  µg/m³

2182      Paris      FR  2019-06-03 09:00:00+00:00  ...       no2  46.0  µg/m³

2183      Paris      FR  2019-06-03 08:00:00+00:00  ...       no2  43.9  µg/m³

2184      Paris      FR  2019-06-03 07:00:00+00:00  ...       no2  50.0  µg/m³

2185      Paris      FR  2019-06-03 06:00:00+00:00  ...       no2  44.1  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

3554  Antwerpen      BE  2019-05-19 15:00:00+00:00  ...       no2  33.0  µg/m³

3555  Antwerpen      BE  2019-05-19 14:00:00+00:00  ...       no2  23.0  µg/m³

3556  Antwerpen      BE  2019-05-19 13:00:00+00:00  ...       no2  14.5  µg/m³

3548  Antwerpen      BE  2019-05-19 21:00:00+00:00  ...       no2  12.5  µg/m³

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

[5272 rows x 7 columns]

d_insert_column_before_another

df

Out[6]: 

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.d_insert_column_before_another(df.city + df.country, 'city_country', 'value')

Out[7]: 

           city country                   date.utc  ... city_country value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...  AntwerpenBE  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...  AntwerpenBE   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...  AntwerpenBE  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...  AntwerpenBE  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...  AntwerpenBE   7.5  µg/m³

         ...     ...                        ...  ...          ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...     LondonGB  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...     LondonGB  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...     LondonGB  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...     LondonGB  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...     LondonGB  67.0  µg/m³

[5272 rows x 8 columns]

df.ds_reverse

df

Out[3]: 

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.ds_reverse()

Out[4]: 

           city country                   date.utc  ... parameter value   unit

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

[5272 rows x 7 columns]

df.d_add_prefix_to_column_when_regex_match

df

Out[8]: 

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.d_add_prefix_to_column_when_regex_match(prefix='aa_', regular_expression='^c')

Out[9]: 

        aa_city aa_country                   date.utc  ... parameter value   unit

0     Antwerpen         BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen         BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen         BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen         BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen         BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...        ...                        ...  ...       ...   ...    ...

5267     London         GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London         GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London         GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London         GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London         GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.d_add_prefix_to_index_when_regex_match

Out[12]: 

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.d_add_prefix_to_index_when_regex_match('five_', regular_expression='^5')

Out[13]: 

                city country                   date.utc  ... parameter value   unit

0          Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1          Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2          Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3          Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4          Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

              ...     ...                        ...  ...       ...   ...    ...

five_5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

five_5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

five_5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

five_5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

five_5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.d_filter_df_by_regex_in_columns

df

Out[14]: 

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.d_filter_df_by_regex_in_columns('^[cu]')

Out[15]: 

           city country   unit

0     Antwerpen      BE  µg/m³

1     Antwerpen      BE  µg/m³

2     Antwerpen      BE  µg/m³

3     Antwerpen      BE  µg/m³

4     Antwerpen      BE  µg/m³

         ...     ...    ...

5267     London      GB  µg/m³

5268     London      GB  µg/m³

5269     London      GB  µg/m³

5270     London      GB  µg/m³

5271     London      GB  µg/m³

[5272 rows x 3 columns]

df.d_filter_df_by_regex_in_index

df

Out[16]: 

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.d_filter_df_by_regex_in_index(r'^5\d\d0$')

Out[17]: 

        city country                   date.utc  ... parameter value   unit

5000  London      GB  2019-04-20 16:00:00+00:00  ...       no2  48.0  µg/m³

5010  London      GB  2019-04-20 06:00:00+00:00  ...       no2  33.0  µg/m³

5020  London      GB  2019-04-19 20:00:00+00:00  ...       no2  58.0  µg/m³

5030  London      GB  2019-04-19 10:00:00+00:00  ...       no2  44.0  µg/m³

5040  London      GB  2019-04-18 23:00:00+00:00  ...       no2  61.0  µg/m³

5050  London      GB  2019-04-18 13:00:00+00:00  ...       no2  49.0  µg/m³

5060  London      GB  2019-04-18 03:00:00+00:00  ...       no2  50.0  µg/m³

5070  London      GB  2019-04-17 17:00:00+00:00  ...       no2  54.0  µg/m³

5080  London      GB  2019-04-17 07:00:00+00:00  ...       no2  51.0  µg/m³

5090  London      GB  2019-04-16 20:00:00+00:00  ...       no2  83.0  µg/m³

5100  London      GB  2019-04-16 09:00:00+00:00  ...       no2  66.0  µg/m³

5110  London      GB  2019-04-15 22:00:00+00:00  ...       no2  47.0  µg/m³

5120  London      GB  2019-04-15 12:00:00+00:00  ...       no2  27.0  µg/m³

5130  London      GB  2019-04-15 02:00:00+00:00  ...       no2  32.0  µg/m³

5140  London      GB  2019-04-14 16:00:00+00:00  ...       no2  23.0  µg/m³

5150  London      GB  2019-04-14 06:00:00+00:00  ...       no2  35.0  µg/m³

5160  London      GB  2019-04-13 20:00:00+00:00  ...       no2  29.0  µg/m³

5170  London      GB  2019-04-13 10:00:00+00:00  ...       no2  45.0  µg/m³

5180  London      GB  2019-04-13 00:00:00+00:00  ...       no2  29.0  µg/m³

5190  London      GB  2019-04-12 14:00:00+00:00  ...       no2  39.0  µg/m³

5200  London      GB  2019-04-12 04:00:00+00:00  ...       no2  33.0  µg/m³

5210  London      GB  2019-04-11 16:00:00+00:00  ...       no2  34.0  µg/m³

5220  London      GB  2019-04-11 06:00:00+00:00  ...       no2  46.0  µg/m³

5230  London      GB  2019-04-10 19:00:00+00:00  ...       no2  35.0  µg/m³

5240  London      GB  2019-04-10 09:00:00+00:00  ...       no2  35.0  µg/m³

5250  London      GB  2019-04-09 23:00:00+00:00  ...       no2  38.0  µg/m³

5260  London      GB  2019-04-09 13:00:00+00:00  ...       no2  56.0  µg/m³

5270  London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

[28 rows x 7 columns]

df.d_columns_upper, df.d_columns_lower,df.d_make_columns_dot_compatible,df.d_index_upper,df.d_index_lower

print(df.columns)

print(df.d_make_columns_dot_compatible().columns)

print(df.d_columns_upper().columns)

print(df.d_columns_lower().columns)

df2=df.copy()

df2.index = df2.parameter

print(df2.index)

print(df2.d_index_upper().index)

print(df2.d_index_lower().index)

Index(['city', 'country', 'date.utc', 'location', 'parameter', 'value',

       'unit'],

      dtype='object')

Index(['city', 'country', 'date_utc', 'location', 'parameter', 'value',

       'unit'],

      dtype='object')

Index(['CITY', 'COUNTRY', 'DATE_UTC', 'LOCATION', 'PARAMETER', 'VALUE',

       'UNIT'],

      dtype='object')

Index(['city', 'country', 'date_utc', 'location', 'parameter', 'value',

       'unit'],

      dtype='object')

Index(['pm25', 'pm25', 'pm25', 'pm25', 'pm25', 'pm25', 'pm25', 'pm25', 'pm25',

       'pm25',

       ...

       'no2', 'no2', 'no2', 'no2', 'no2', 'no2', 'no2', 'no2', 'no2', 'no2'],

      dtype='object', name='parameter', length=5272)

Index(['PM25', 'PM25', 'PM25', 'PM25', 'PM25', 'PM25', 'PM25', 'PM25', 'PM25',

       'PM25',

       ...

       'NO2', 'NO2', 'NO2', 'NO2', 'NO2', 'NO2', 'NO2', 'NO2', 'NO2', 'NO2'],

      dtype='object', length=5272)

Index(['pm25', 'pm25', 'pm25', 'pm25', 'pm25', 'pm25', 'pm25', 'pm25', 'pm25',

       'pm25',

       ...

       'no2', 'no2', 'no2', 'no2', 'no2', 'no2', 'no2', 'no2', 'no2', 'no2'],

      dtype='object', length=5272)

df.d_natsort_index

df2 = df.sample(len(df)).copy()

dftempindex = df2.index[:2500].to_list()

tempvalue = df2.loc[dftempindex].parameter.copy()

tempvalue = tempvalue.apply(lambda x: str(x).upper())

df2.loc[dftempindex, 'parameter'] = tempvalue

print(df2)

df2.index = df2.parameter

print(df2.d_natsort_index())

for a,b,c,d in zip(df2.d_natsort_index(sort_numbers_after_non_numbers=True).index.to_list(), df2.d_natsort_index(lowercase_first=True).index.to_list(),df2.d_natsort_index(group_lower_and_uppercase=True).index.to_list(),df2.d_natsort_index(uppercase_first=True).index.to_list()):

    print(a,b,c,d)



no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 no2 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

no2 NO2 PM25 no2

df.d_natsort_df_by_column

df

Out[3]: 

           city country                   date.utc  ... parameter value   unit

0     Antwerpen      BE  2019-06-18 06:00:00+00:00  ...      pm25  18.0  µg/m³

1     Antwerpen      BE  2019-06-17 08:00:00+00:00  ...      pm25   6.5  µg/m³

2     Antwerpen      BE  2019-06-17 07:00:00+00:00  ...      pm25  18.5  µg/m³

3     Antwerpen      BE  2019-06-17 06:00:00+00:00  ...      pm25  16.0  µg/m³

4     Antwerpen      BE  2019-06-17 05:00:00+00:00  ...      pm25   7.5  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

5267     London      GB  2019-04-09 06:00:00+00:00  ...       no2  41.0  µg/m³

5268     London      GB  2019-04-09 05:00:00+00:00  ...       no2  41.0  µg/m³

5269     London      GB  2019-04-09 04:00:00+00:00  ...       no2  41.0  µg/m³

5270     London      GB  2019-04-09 03:00:00+00:00  ...       no2  67.0  µg/m³

5271     London      GB  2019-04-09 02:00:00+00:00  ...       no2  67.0  µg/m³

[5272 rows x 7 columns]

df.d_natsort_df_by_column('date.utc')

Out[4]: 

           city country                   date.utc  ... parameter value   unit

176   Antwerpen      BE  2019-04-09 01:00:00+00:00  ...      pm25  76.0  µg/m³

3500      Paris      FR  2019-04-09 01:00:00+00:00  ...       no2  24.4  µg/m³

3663  Antwerpen      BE  2019-04-09 01:00:00+00:00  ...       no2  22.5  µg/m³

175   Antwerpen      BE  2019-04-09 02:00:00+00:00  ...      pm25  91.5  µg/m³

1824     London      GB  2019-04-09 02:00:00+00:00  ...      pm25  42.0  µg/m³

         ...     ...                        ...  ...       ...   ...    ...

1827      Paris      FR  2019-06-20 22:00:00+00:00  ...       no2  26.5  µg/m³

178      London      GB  2019-06-20 23:00:00+00:00  ...      pm25   7.0  µg/m³

1826      Paris      FR  2019-06-20 23:00:00+00:00  ...       no2  21.8  µg/m³

177      London      GB  2019-06-21 00:00:00+00:00  ...      pm25   7.0  µg/m³

1825      Paris      FR  2019-06-21 00:00:00+00:00  ...       no2  20.0  µg/m³

[5272 rows x 7 columns]

df.d_rename_columns / df.d_rename_index

df = pd.read_csv(    "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")



print(df)

     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked

0              1         0       3  ...   7.2500   NaN         S

1              2         1       1  ...  71.2833   C85         C

2              3         1       3  ...   7.9250   NaN         S

3              4         1       1  ...  53.1000  C123         S

4              5         0       3  ...   8.0500   NaN         S

..           ...       ...     ...  ...      ...   ...       ...

886          887         0       2  ...  13.0000   NaN         S

887          888         1       1  ...  30.0000   B42         S

888          889         0       3  ...  23.4500   NaN         S

889          890         1       1  ...  30.0000  C148         C

890          891         0       3  ...   7.7500   NaN         Q





df.d_rename_columns(Fare='Embarked',Embarked='Fare',Cabin='cabin2')

df.d_rename_index({1: 1000000,2:50022})

print(df)



         PassengerId  Survived  Pclass  ... Embarked cabin2  Fare

0                  1         0       3  ...   7.2500    NaN     S

1000000            2         1       1  ...  71.2833    C85     C

50022              3         1       3  ...   7.9250    NaN     S

3                  4         1       1  ...  53.1000   C123     S

4                  5         0       3  ...   8.0500    NaN     S

              ...       ...     ...  ...      ...    ...   ...

886              887         0       2  ...  13.0000    NaN     S

887              888         1       1  ...  30.0000    B42     S

888              889         0       3  ...  23.4500    NaN     S

889              890         1       1  ...  30.0000   C148     C

890              891         0       3  ...   7.7500    NaN     Q

[891 rows x 12 columns]



df.d_rename_columns({'Embarked': 'Fare', 'Fare' : 'Embarked', 'cabin2' : 'Cabin'})

df.index = df.index.astype('string')

df.index = 'a' + df.index

df.d_rename_index(a1000000= 1,a50022=2)

print(df)



      PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked

a0              1         0       3  ...   7.2500   NaN         S

1               2         1       1  ...  71.2833   C85         C

2               3         1       3  ...   7.9250   NaN         S

a3              4         1       1  ...  53.1000  C123         S

a4              5         0       3  ...   8.0500   NaN         S

           ...       ...     ...  ...      ...   ...       ...

a886          887         0       2  ...  13.0000   NaN         S

a887          888         1       1  ...  30.0000   B42         S

a888          889         0       3  ...  23.4500   NaN         S

a889          890         1       1  ...  30.0000  C148         C

a890          891         0       3  ...   7.7500   NaN         Q

Metadata

Release files for a-pandas-ex-columns-and-index 0.13

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for a-pandas-ex-columns-and-index 0.13
File	Size	Uploaded	Provenance	Actions
a_pandas_ex_columns_and_index-0.13.tar.gz	16.0 kB	Oct 8, 2022	Without provenance	Details for a_pandas_ex_columns_and_index-0.13.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for a-pandas-ex-columns-and-index 0.13
File	Interpreter	ABI	Platform	Actions Reset
a_pandas_ex_columns_and_index-0.13-py3-none-any.whl 13.2 kB Oct 8, 2022 Without provenance	Python 3	none	any	Details for a_pandas_ex_columns_and_index-0.13-py3-none-any.whl

Total release size: 29.2 kB

Release files / a_pandas_ex_columns_and_index-0.13.tar.gz

Download URL	a_pandas_ex_columns_and_index-0.13.tar.gz
Size	16.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`6e5419e62d9a8eb9d33b16b5e9ea3e97d421a67ad1afd6fb3c55766d812762a0`
BLAKE2b-256 checksum How to use checksums	`d184b7e02dd5436b2f85a5b2bdfd5695b1e6b2785949aed88475a44ed057fe8d`
Upload date	Oct 8, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.9.13`

Release files / a_pandas_ex_columns_and_index-0.13-py3-none-any.whl

Download URL	a_pandas_ex_columns_and_index-0.13-py3-none-any.whl
Size	13.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`11ffaffdc851db9fd35fd1642b4199a0a39c19560b76394f2ea46b31f95a97bc`
BLAKE2b-256 checksum How to use checksums	`79ec99b936f293932c1f32c0598f48c5db2b4022445b03596a2f0165d536b4c9`
Upload date	Oct 8, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.9.13`

Security for a-pandas-ex-columns-and-index 0.13

Report project as malware

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

a-pandas-ex-columns-and-index 0.13