import pandas as pd
url = 'http://www.igobychad.com/test_table.html'
for i, df in enumerate(pd.read_html(url)):
df.to_csv('myfile_{}.csv'.format(i))
df = pd.read_csv('myfile_0.csv')
df
Unnamed: 0 | Category | June2016 | Apr.2017 | May2017 | June2017 | Change from:May2017-June2017 | |
---|---|---|---|---|---|---|---|
0 | 0 | Estatus | NaN | NaN | NaN | NaN | NaN |
1 | 1 | CN pop | 253397.0 | 254588.0 | 254767.0 | 254957.0 | 190.0 |
2 | 2 | Clf | 158889.0 | 160213.0 | 159784.0 | 160145.0 | 361.0 |
3 | 3 | Prate | 62.7 | 62.9 | 62.7 | 62.8 | 0.1 |
4 | 4 | Em | 151090.0 | 153156.0 | 152923.0 | 153168.0 | 245.0 |
5 | 5 | Ep ratio | 59.6 | 60.2 | 60.0 | 60.1 | 0.1 |
6 | 6 | Unem | 7799.0 | 7056.0 | 6861.0 | 6977.0 | 116.0 |
7 | 7 | Un rate | 4.9 | 4.4 | 4.3 | 4.4 | 0.1 |
8 | 8 | - Over-the-month changes | NaN | NaN | NaN | NaN | NaN |
To clean up NaN can use dropna()
df.dropna()
Unnamed: 0 | Category | June2016 | Apr.2017 | May2017 | June2017 | Change from:May2017-June2017 | |
---|---|---|---|---|---|---|---|
1 | 1 | CN pop | 253397.0 | 254588.0 | 254767.0 | 254957.0 | 190.0 |
2 | 2 | Clf | 158889.0 | 160213.0 | 159784.0 | 160145.0 | 361.0 |
3 | 3 | Prate | 62.7 | 62.9 | 62.7 | 62.8 | 0.1 |
4 | 4 | Em | 151090.0 | 153156.0 | 152923.0 | 153168.0 | 245.0 |
5 | 5 | Ep ratio | 59.6 | 60.2 | 60.0 | 60.1 | 0.1 |
6 | 6 | Unem | 7799.0 | 7056.0 | 6861.0 | 6977.0 | 116.0 |
7 | 7 | Un rate | 4.9 | 4.4 | 4.3 | 4.4 | 0.1 |