%matplotlib inline
import pandas as pd
data = pd.read_csv("emdata-tsv (1).csv")
data.describe()
data.shape
data.Country.describe()
data.Type.describe()
count 17828 unique 15 top Transport Accident freq 4351 Name: Type, dtype: object
data.describe()
Start | End | Duration | Killed | Cost | Affected | Column 12 | |
---|---|---|---|---|---|---|---|
count | 17828.000000 | 17828.000000 | 17828.000000 | 13996.000000 | 1.108500e+04 | 3772.000000 | 0 |
mean | 1990.883049 | 1990.918387 | 0.035338 | 2718.355173 | 5.586713e+05 | 488.759659 | NaN |
std | 17.851370 | 17.836943 | 0.302913 | 75162.832728 | 6.951056e+06 | 3384.235083 | NaN |
min | 1900.000000 | 1900.000000 | 0.000000 | 1.000000 | 1.000000e+00 | 0.003000 | NaN |
25% | 1986.000000 | 1986.000000 | 0.000000 | 12.000000 | 6.000000e+01 | 5.000000 | NaN |
50% | 1996.000000 | 1996.000000 | 0.000000 | 24.000000 | 1.000000e+03 | 35.000000 | NaN |
75% | 2003.000000 | 2003.000000 | 0.000000 | 57.000000 | 1.975000e+04 | 200.000000 | NaN |
max | 2008.000000 | 2009.000000 | 9.000000 | 5000000.000000 | 3.000000e+08 | 125000.000000 | NaN |
8 rows × 7 columns
print data.Killed.groupby(data.Type).sum().order()
Type Wildfire 3287 Mass movement dry 4919 Industrial Accident 49797 Mass movement wet 55040 Miscellaneous accident 58121 Volcano 95979 Extreme temperature 108938 Transport Accident 201053 Storm 1373104 Earthquake (seismic activity) 2311491 Complex Disasters 5610000 Flood 6911040 Epidemic 9555059 Drought 11708271 Insect infestation NaN dtype: float64