获取熊猫列的平均值

Question

I'm trying to be able to read a file where I will pull what the name of the location is and then calculate the average amount of snow they get. This is what I have so far.

import pandas
data = pandas.read_csv('filteredData.csv')
if ('NAME' == 'ADA 0.7 SE, MI US'):
    data.ix['1/1/2016':'12/31/2016']
    newdata=data['SNOW'].mean()

I am still unsure if doing this would be better by grouping the name of the locations and then calculating the average snow that way.

Please be patient with me I'm still brand new to pandas.

This image is just one part of twenty different locations:

Answer 1

Looks like you can use groupby() :

import pandas as pd
from datetime import datetime

# Create test data
df = pd.DataFrame({
    "name": ["place1", "place1", "place1", "place2", "place2", "place2"] * 2,
    "date": ["1/1/2016", "1/2/2016", "1/3/2016"] * 2 + ["1/1/2017", "1/2/2017", "1/3/2017"] * 2,
    "snow": [10.0, 20.0, 30.0, 100.0, 200.0, 300.0, 0.0, 1.0, 2.0, 1000.0, 2000.0, 3000.0]
    })
# Transform string date to datetime format
df["date"] = pd.to_datetime(df["date"])

df looks like:

         date    name    snow
0  2016-01-01  place1    10.0
1  2016-01-02  place1    20.0
2  2016-01-03  place1    30.0
3  2016-01-01  place2   100.0
4  2016-01-02  place2   200.0
5  2016-01-03  place2   300.0
6  2017-01-01  place1     0.0
7  2017-01-02  place1     1.0
8  2017-01-03  place1     2.0
9  2017-01-01  place2  1000.0
10 2017-01-02  place2  2000.0
11 2017-01-03  place2  3000.0

Then calculate the average amount of snow for a certain group, in this case, I've grouped by name and year:

# Group dataset by name of place and year, calculate the average amount of snow for each group
df["snow_average"] = df.groupby(["name", df.date.dt.year])["snow"].transform("mean")

df now looks like:

         date    name    snow  snow_average
0  2016-01-01  place1    10.0          20.0
1  2016-01-02  place1    20.0          20.0
2  2016-01-03  place1    30.0          20.0
3  2016-01-01  place2   100.0         200.0
4  2016-01-02  place2   200.0         200.0
5  2016-01-03  place2   300.0         200.0
6  2017-01-01  place1     0.0           1.0
7  2017-01-02  place1     1.0           1.0
8  2017-01-03  place1     2.0           1.0
9  2017-01-01  place2  1000.0        2000.0
10 2017-01-02  place2  2000.0        2000.0
11 2017-01-03  place2  3000.0        2000.0

You can change your groupby() criteria according to what you're looking for. I used name and year because that's what it looks like you want, judging by your example.

EDIT: My apologies, I got frustrated. You seem to have misunderstood my answer, and that you needed to create a new dataframe manually. You can use your dataset from the csv file, and do the same commands as above, but apply them to your dataset, which you call data :

import pandas
data = pandas.read_csv('filteredData.csv')

# Transform string date to datetime format
data["date"] = pd.to_datetime(data["date"])
print data

# Group dataset by name of place and year, calculate the average amount of snow for each group
data["snow_average"] = data.groupby(["name", data.date.dt.year])["snow"].transform("mean")
print data