pandas 如何使用 groupby 获得熊猫的月均值
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How get monthly mean in pandas using groupby
提问by user1345283
I have the next DataFrame:
我有下一个数据帧：
data=pd.read_csv('anual.csv', parse_dates='Fecha', index_col=0)
data
DatetimeIndex: 290 entries, 2011-01-01 00:00:00 to 2011-12-31 00:00:00
Data columns (total 12 columns):
HR 290 non-null values
PreciAcu 290 non-null values
RadSolar 290 non-null values
T 290 non-null values
Presion 290 non-null values
Tmax 290 non-null values
HRmax 290 non-null values
Presionmax 290 non-null values
RadSolarmax 290 non-null values
Tmin 290 non-null values
HRmin 290 non-null values
Presionmin 290 non-null values
dtypes: float64(4), int64(8)
where:
在哪里：
data['HR']
Fecha
2011-01-01 37
2011-02-01 70
2011-03-01 62
2011-04-01 69
2011-05-01 72
2011-06-01 71
2011-07-01 71
2011-08-01 70
2011-09-01 40
...
2011-12-17 92
2011-12-18 78
2011-12-19 79
2011-12-20 76
2011-12-21 78
2011-12-22 80
2011-12-23 72
2011-12-24 70
In addition, some months are not always complete. My goal is to calculate the average of each month from daily data. This is achieved as follows:
此外，有些月份并不总是完整的。我的目标是根据每日数据计算每个月的平均值。这是通过以下方式实现的：
monthly=data.resample('M', how='mean')
HR PreciAcu RadSolar T Presion Tmax
Fecha
2011-01-31 68.586207 3.744828 163.379310 17.496552 0 25.875862
2011-02-28 68.666667 1.966667 208.000000 18.854167 0 28.879167
2011-03-31 69.136364 3.495455 218.090909 20.986364 0 30.359091
2011-04-30 68.956522 1.913043 221.130435 22.165217 0 31.708696
2011-05-31 72.700000 0.550000 201.100000 18.900000 0 27.460000
2011-06-30 70.821429 6.050000 214.000000 23.032143 0 30.621429
2011-07-31 78.034483 5.810345 188.206897 21.503448 0 27.951724
2011-08-31 71.750000 1.028571 214.750000 22.439286 0 30.657143
2011-09-30 72.481481 0.185185 196.962963 21.714815 0 29.596296
2011-10-31 68.083333 1.770833 224.958333 18.683333 0 27.075000
2011-11-30 71.750000 0.812500 169.625000 18.925000 0 26.237500
2011-12-31 71.833333 0.160000 159.533333 17.260000 0 25.403333
The first error I find is in the column of precipitation, since all observations are 0 in January and an average of 3.74 is obtained for this particular month.
我发现的第一个错误是在降水列中，因为一月份的所有观测值都是 0，而这个特定月份的平均值为 3.74。
When averages in Excel and compare them with the results above, there is significant variation. For Example, the mean of HR for Febrero is
当在 Excel 中取平均值并将它们与上述结果进行比较时，存在显着差异。例如，Febrero 的 HR 平均值为
mean HR using pandas=68.66
mean HR using excel=67
Another detail I found:
我发现的另一个细节：
data['PreciAcu']['2011-01'].count()
29 and should be 31
Am I doing something wrong? How I can fix this error?
难道我做错了什么？我该如何解决这个错误？
Annex csv file:
附件csv文件：
回答by DSM
Your date column is being misinterpreted, because it's in DD/MM/YYYY format. Set dayfirst=Trueinstead:
您的日期列被误解了，因为它是 DD/MM/YYYY 格式。dayfirst=True改为设置：
>>> df = pd.read_csv('anual.csv', parse_dates='Fecha', dayfirst=True, index_col=0, sep="\s+")
>>> df['PreciAcu']['2011-01'].count()
31
>>> df.resample("M").mean()
HR PreciAcu RadSolar T Presion Tmax \
Fecha
2011-01-31 68.774194 0.000000 162.354839 16.535484 0 25.393548
2011-02-28 67.000000 0.000000 193.481481 15.418519 0 25.696296
2011-03-31 59.083333 0.850000 254.541667 21.295833 0 32.325000
2011-04-30 61.200000 1.312000 260.640000 24.676000 0 34.760000
2011-05-31 NaN NaN NaN NaN NaN NaN
2011-06-30 68.428571 8.576190 236.619048 25.009524 0 32.028571
2011-07-31 81.518519 11.488889 185.407407 22.429630 0 27.681481
2011-08-31 76.451613 0.677419 219.645161 23.677419 0 30.719355
2011-09-30 77.533333 2.883333 196.100000 21.573333 0 28.723333
2011-10-31 73.120000 1.260000 196.280000 19.552000 0 27.636000
2011-11-30 71.277778 -79.333333 148.555556 18.250000 0 26.511111
2011-12-31 73.741935 0.067742 134.677419 15.687097 0 24.019355
HRmax Presionmax Tmin
Fecha
2011-01-31 92.709677 0 10.909677
2011-02-28 92.111111 0 8.325926
2011-03-31 89.291667 0 13.037500
2011-04-30 89.400000 0 17.328000
2011-05-31 NaN NaN NaN
2011-06-30 92.095238 0 19.761905
2011-07-31 97.185185 0 18.774074
2011-08-31 96.903226 0 18.670968
2011-09-30 97.200000 0 16.373333
2011-10-31 97.000000 0 13.412000
2011-11-30 94.555556 0 11.877778
2011-12-31 94.161290 0 10.070968
[12 rows x 9 columns]
(Note, though - I'd forgotten this -- that dayfirst=Trueisn't strict, see here. Maybe using date_parserwould be safer.)
（但请注意 - 我忘记了这一点 - 这dayfirst=True并不严格，请参阅此处。也许使用date_parser会更安全。）
