In [1]:

%load_ext autoreload
%autoreload 2

In [2]:

import pandas as pd
df = pd.read_csv('./bike_sharing_dc.csv', parse_dates=['date'])[:10]
strange_name = f"{''.join([chr(i) for i in range(1, 256)])}"
df[strange_name] = df['date']
df = df.set_index([strange_name, 'date'])
df.iloc[1, 4] = strange_name
df

Out[2]:

		month	season	hour	year	holiday	temperature	feeling_temp	humidity	winspeed	casual	registered	count	work yes or not	am or pm	Day of the week
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{\|}~ ¡¢£¤¥¦§¨©ª«¬®¯°±²³´µ¶·¸¹º»¼½¾¿ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûüýþÿ	date
2011-01-01	2011-01-01	1	winter	0	2011	no	3.28	3.0014	81.0	0.0000	3	13	16	0	am	6
	2011-01-01	1	winter	1	2011	\t\n\r !"#$%&'()*+...	2.34	1.9982	80.0	0.0000	8	32	40	0	am	6
	2011-01-01	1	winter	2	2011	no	2.34	1.9982	80.0	0.0000	5	27	32	0	am	6
	2011-01-01	1	winter	3	2011	no	3.28	3.0014	75.0	0.0000	3	10	13	0	am	6
	2011-01-01	1	winter	4	2011	no	3.28	3.0014	75.0	0.0000	0	1	1	0	am	6
	2011-01-01	1	winter	5	2011	no	3.28	1.0016	75.0	6.0032	0	1	1	0	am	6
	2011-01-01	1	winter	6	2011	no	2.34	1.9982	80.0	0.0000	2	0	2	0	am	6
	2011-01-01	1	winter	7	2011	no	1.40	1.0016	86.0	0.0000	1	2	3	0	am	6
	2011-01-01	1	winter	8	2011	no	3.28	3.0014	75.0	0.0000	1	7	8	0	am	6
	2011-01-01	1	winter	9	2011	no	7.04	7.0010	76.0	0.0000	8	6	14	0	am	6

In [3]:

import pygwalker as pyg
pyg.walk(df)

In [ ]: