In [1]:
import pandas as pd

In [6]:
df = pd.read_csv('wssinfo_data_cleaned.csv')

In [8]:
df.head()


Out[8]:
Country Year Population Urban (x1000) Population Urban (% Total) Population Rural (x1000) Population Rural (% Total) Population (x 1000) Water Urban Total Improved (x1000) Water Urban Total Improved (%) Water Urban Piped on Premises (x1000) ... Sanitation National Total Improved (x1000) Sanitation National Total Improved (%) Sanitation National Total Unimproved (x1000) Sanitation National Total Unimproved (%) Sanitation National Shared (x1000) Sanitation National Shared (%) Sanitation National Other Unimproved (x1000) Sanitation National Other Unimproved (%) Sanitation National Open Defecation (x1000) Sanitation National Open Defecation (%)
0 Afghanistan 1990 2130.0 18.2 9601.2 81.8 11731.2 NaN NaN 65.7 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 Afghanistan 1991 2319.2 18.4 10292.8 81.6 12612.0 324.0 14 71.6 ... 2640.0 20.9 9972.1 79.1 951.3 7.5 4817.9 38.2 4202.9 33.324284
2 Afghanistan 1992 2572.3 18.6 11239.6 81.4 13811.9 359.4 14 79.4 ... 2893.1 20.9 10918.7 79.1 1043.5 7.6 5279.5 38.2 4595.8 33.274234
3 Afghanistan 1993 2862.1 18.9 12313.3 81.1 15175.3 399.8 14 88.3 ... 3181.0 21.0 11994.4 79.0 1148.3 7.6 5804.2 38.2 5041.8 33.223974
4 Afghanistan 1994 3148.5 19.1 13336.5 80.9 16485.0 439.9 14 97.1 ... 3458.0 21.0 13027.1 79.0 1249.3 7.6 6309.1 38.3 5468.6 33.173067

5 rows × 73 columns


In [ ]: