[Python]Dataframe GruopBy sum

In [19]:

import pandas as pd

In [20]:

df = pd.read_csv('weather_by_cities.csv')

Out[20]:

	day	city	temperature	windspeed	event
0	1/1/2017	new york	32	6	Rain
1	1/2/2017	new york	36	7	Sunny
2	1/3/2017	new york	28	12	Snow
3	1/4/2017	new york	33	7	Sunny
4	1/1/2017	mumbai	90	5	Sunny
5	1/2/2017	mumbai	85	12	Fog
6	1/3/2017	mumbai	87	15	Fog
7	1/4/2017	mumbai	92	5	Rain
8	1/1/2017	paris	45	20	Sunny
9	1/2/2017	paris	50	13	Cloudy
10	1/3/2017	paris	54	8	Cloudy
11	1/4/2017	paris	42	10	Cloudy

In [55]:

g = df.groupby('city')
g

Out[55]:

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000200CAE08588>

Group BY¶

In [56]:

for city, city_df in g:
    print(city)
    print(city_df)

mumbai
        day    city  temperature  windspeed  event
4  1/1/2017  mumbai           90          5  Sunny
5  1/2/2017  mumbai           85         12    Fog
6  1/3/2017  mumbai           87         15    Fog
7  1/4/2017  mumbai           92          5   Rain
new york
        day      city  temperature  windspeed  event
0  1/1/2017  new york           32          6   Rain
1  1/2/2017  new york           36          7  Sunny
2  1/3/2017  new york           28         12   Snow
3  1/4/2017  new york           33          7  Sunny
paris
         day   city  temperature  windspeed   event
8   1/1/2017  paris           45         20   Sunny
9   1/2/2017  paris           50         13  Cloudy
10  1/3/2017  paris           54          8  Cloudy
11  1/4/2017  paris           42         10  Cloudy

In [48]:

g = df.groupby('city').sum()

Out[48]:

	temperature	windspeed
city
mumbai	354	37
new york	129	32
paris	191	51

In [23]:

# SELECT * from city_data GROUP BY city
g.get_group('mumbai')

Out[23]:

	day	city	temperature	windspeed	event
4	1/1/2017	mumbai	90	5	Sunny
5	1/2/2017	mumbai	85	12	Fog
6	1/3/2017	mumbai	87	15	Fog
7	1/4/2017	mumbai	92	5	Rain

In [33]:

g.max()

Out[33]:

	day	temperature	windspeed	event
city
mumbai	1/4/2017	92	15	Sunny
new york	1/4/2017	36	12	Sunny
paris	1/4/2017	54	20	Sunny

In [35]:

g.mean()

Out[35]:

	temperature	windspeed
city
mumbai	88.50	9.25
new york	32.25	8.00
paris	47.75	12.75

In [38]:

g.describe()

Out[38]:

	temperature								windspeed
	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max
city
mumbai	4.0	88.50	3.109126	85.0	86.50	88.5	90.50	92.0	4.0	9.25	5.057997	5.0	5.00	8.5	12.75	15.0
new york	4.0	32.25	3.304038	28.0	31.00	32.5	33.75	36.0	4.0	8.00	2.708013	6.0	6.75	7.0	8.25	12.0
paris	4.0	47.75	5.315073	42.0	44.25	47.5	51.00	54.0	4.0	12.75	5.251984	8.0	9.50	11.5	14.75	20.0