情景：计算两次考试，所有学生的平均成绩：

 

'''a.csv
date,name,grade
2020-07-01,mary,70
2020-07-01,tom,80
2020-07-01,jerry,70
2020-07-02,mary,80
2020-07-02,tom,90
2020-07-02,tom,100'''

grade = pd.read_csv('C:\\test\\a.csv')

# average grade at 0701 and 0702
avg = grade['grade'].groupby(grade['date']).mean()

# format grade avg to 'avg: XX'
import math
avg_new = avg.apply(lambda x: 'avg: '+str(math.floor(x))) 

# last step, change avg_new to key-value
avg_new.to_dict()

结果：





 

Python中如何求DataFrame中某一列数的日平均值？

例如求df对象中close列的5日均线：

1.使用循环，采用手动计算的方式：

df['close'][i-4:i+1].mean()

获取第i天的5日均线

2.对整个dataframe对象，使用rolling函数：

daily['close'].rolling(5).mean()

其中rolling[5]返回的是一个rolling对象，包含5行数据的对象，因此需要想(1)中一样使用mean，获取平均值。

获取每一条记录的5日均值。

I have a pandas dataframe that looks like this
ID     country   month   revenue  profit   ebit
234    USA       201409   10        5       3
344    USA       201409    9        7       2
532    UK        201410    20       10      5
129    Canada    201411    15       10      5
I want to group by ID, country, month and count the IDs per month and country and sum the revenue, profit, ebit.
The output for the above data would be:
country   month    revenue   profit  ebit   count
USA     201409     19        12      5      2
UK      201409     20        10      5      1
Canada  201411     15        10      5      1
I have tried different variations of groupby, sum and count functions of pandas but I am unable to figure out how to apply groupby sum and count all together to give the result as shown. Please share any ideas that you might have. Thanks!
解决方案
It can be done using pivot_table this way:
>>> df1=pd.pivot_table(df, index=['country','month'],values=['revenue','profit','ebit'],aggfunc=np.sum)
>>> df1
ebit  profit  revenue
country month
Canada  201411     5      10       15
UK      201410     5      10       20
USA     201409     5      12       19
>>> df2=pd.pivot_table(df, index=['country','month'], values='ID',aggfunc=len).rename('count')
>>> df2
country  month
Canada   201411    1
UK       201410    1
USA      201409    2
>>> pd.concat([df1,df2],axis=1)
ebit  profit  revenue  count
country month
Canada  201411     5      10       15      1
UK      201410     5      10       20      1
USA     201409     5      12       19      2

我有下一个DataFrame：
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)
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
此外,有些月份并不总是完整的.我的目标是从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
我发现的第一个错误是在降水列中,因为所有观测结果在1月份都是0,而在这个特定月份获得的平均值为3.74.
当Excel中的平均值与上面的结果进行比较时,存在显着差异.例如,Febrero的HR平均值是
mean HR using pandas=68.66
mean HR using excel=67
我找到的另一个细节：
data['PreciAcu']['2011-01'].count()
29 and should be 31
难道我做错了什么？
我该如何解决这个错误？
附件csv文件：