معرفی شرکت ها
a-pandas-ex-columns-and-index-0.13
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Some useful methods for columns / index in Pandas DataFrames
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | a-pandas-ex-columns-and-index-0.13 |
| نام | a-pandas-ex-columns-and-index |
| نسخه کتابخانه | 0.13 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Johannes Fischer |
| ایمیل نویسنده | <aulasparticularesdealemaosp@gmail.com> |
| آدرس صفحه اصلی | https://github.com/hansalemaos/a_pandas_ex_columns_and_index |
| آدرس اینترنتی | https://pypi.org/project/a-pandas-ex-columns-and-index/ |
| مجوز | MIT |
## Some useful Pandas methods for df.index and df.columns
```python
pip install a-pandas-ex-columns-and-index
```
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```
نیازمندی
| مقدار | نام |
|---|---|
| - | flatten-any-dict-iterable-or-whatsoever |
| - | natsort |
| - | numpy |
| - | pandas |
| - | regex |
نحوه نصب
نصب پکیج whl a-pandas-ex-columns-and-index-0.13:
pip install a-pandas-ex-columns-and-index-0.13.whl
نصب پکیج tar.gz a-pandas-ex-columns-and-index-0.13:
pip install a-pandas-ex-columns-and-index-0.13.tar.gz