如何将行添加到数据框中以组织时间序列

Question

I have a data frame that looks like this

        AUX     TER
11/2014 2.0     10.0
01/2015 23.0    117.0
03/2015 57.0    65.0
04/2015 1.0     1.0
05/2015 16.0    20.0
07/2015 19.0    30.0

I want to fill the values for months that are not in data frame with 0 like this

        AUX     TER
11/2014 2.0     10.0
12/2014 0       0
01/2015 23.0    117.0
03/2015 57.0    65.0
04/2015 1.0     1.0
05/2015 16.0    20.0
06/2015 0       0
07/2015 19.0    30.0

Answer 1

1. Change your index to datetime

    df.index = pd.to_datetime(df.index, format='%m/%Y') 

2. Use asfreq with the fill_value argument

    df.asfreq('MS', fill_value=0) AUX TER 2014-11-01 2.0 10.0 2014-12-01 0.0 0.0 2015-01-01 23.0 117.0 2015-02-01 0.0 0.0 2015-03-01 57.0 65.0 2015-04-01 1.0 1.0 2015-05-01 16.0 20.0 2015-06-01 0.0 0.0 2015-07-01 19.0 30.0 

Answer 2

You can use the below to reindex() :

s=pd.to_datetime(df.index)
df.reindex(pd.date_range(s.min(),s.max()+pd.DateOffset(months=1),freq='M')
           .strftime('%m/%Y'),fill_value=0)

---

          AUX    TER
11/2014   2.0   10.0
12/2014   0.0    0.0
01/2015  23.0  117.0
02/2015   0.0    0.0
03/2015  57.0   65.0
04/2015   1.0    1.0
05/2015  16.0   20.0
06/2015   0.0    0.0
07/2015  19.0   30.0

Answer 3

Using df.resample("M").mean().fillna(0)

Ex:

df = pd.read_csv(filename, sep="\s+", parse_dates=['date'])
df.set_index("date", inplace=True)
df = df.resample("M").mean().fillna(0)
df.index = df.index.strftime("%m/%Y")

print(df)

Output:

          AUX    TER
11/2014   2.0   10.0
12/2014   0.0    0.0
01/2015  23.0  117.0
02/2015   0.0    0.0
03/2015  57.0   65.0
04/2015   1.0    1.0
05/2015  16.0   20.0
06/2015   0.0    0.0
07/2015  19.0   30.0

Answer 4

When you have a datetime format, you can try:

df.resample('MS').mean()

following this post: Python, summarize daily data in dataframe to monthly and quarterly