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 [ ]:
Content source: birdsarah/pycon_2015_bokeh_talk
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