基于日期时间列名称的数据框的条件平均值

Question

我是 python 新手。我正在寻找一种方法来根据列名生成行值的平均值（列名是从 1 月到 12 月的日期系列格式）。我想在一年的时间里每 10 天生成一次平均值。我的数据框采用以下格式（2000 行）

import pandas as pd
df= pd.DataFrame({'A':[81,80.09,83,85,88],
                  'B':[21.8,22.04,21.8,21.7,22.06],
                  '20210113':[0,0.05,0,0,0.433],
                  '20210122':[0,0.13,0,0,0.128],
                  '20210125':[0.056,0,0.043,0.062,0.16],
                  '20210213':[0.9,0.56,0.32,0.8,0],
                  '20210217':[0.7,0.99,0.008,0.23,0.56],
                  '20210219':[0.9,0.43,0.76,0.98,0.5]})

预期输出：

In [2]: df
Out[2]: 
   A        B     c(Mean 20210111,..20210119 ) D(Mean of 20210120..20210129)..
0  81       21.8
1  80.09    22.04
2  83       21.8
3  85       21.7           
4  88       22.06

Answer 1

一种方法是将日期列与 DF 的其余部分隔离开来。转置它以能够使用正常的分组操作。然后转回并合并到 DataFrame 中未受影响的部分。

import pandas as pd

df = pd.DataFrame({'A': [81, 80.09, 83, 85, 88],
                   'B': [21.8, 22.04, 21.8, 21.7, 22.06],
                   '20210113A.2': [0, 0.05, 0, 0, 0.433],
                   '20210122B.1': [0, 0.13, 0, 0, 0.128],
                   '20210125C.3': [0.056, 0, 0.043, 0.062, 0.16],
                   '20210213': [0.9, 0.56, 0.32, 0.8, 0],
                   '20210217': [0.7, 0.99, 0.008, 0.23, 0.56],
                   '20210219': [0.9, 0.43, 0.76, 0.98, 0.5]})

# Unaffected Columns Go Here
keep_columns = ['A', 'B']

# Get All Affected Columns
new_df = df.loc[:, ~df.columns.isin(keep_columns)]

# Strip Extra Information From Column Names
new_df.columns = new_df.columns.map(lambda c: c[0:8])

# Transpose
new_df = new_df.T

# Convert index to DateTime for easy use
new_df.index = pd.to_datetime(new_df.index, format='%Y%m%d')

# Resample every 10 Days on new DT index (Drop any rows with no values)
new_df = new_df.resample("10D").mean().dropna(how='all')

# Transpose and Merge Back on DF
df = df[keep_columns].merge(new_df.T, left_index=True, right_index=True)

# For Display
print(df.to_string())

输出：

A B 2021-01-13 00:00:00 2021-01-23 00:00:00 2021-02-12 00:00:00 0 81.00 21.80 0.0000 0.056 0.833333 1 80.09 22.04 0.0900 0.000 0.660000 2 83.00 21.80 0.0000 0.043 0.362667 3 85.00 21.70 0.0000 0.062 0.670000 4 88.00 22.06 0.2805 0.160 0.353333

---

new_df = df.loc[:, ~df.columns.isin(keep_columns)]

new_df

0 1 2 3 4 20210113 0.000 0.05 0.000 0.000 0.433 20210122 0.000 0.13 0.000 0.000 0.128 20210125 0.056 0.00 0.043 0.062 0.160 20210213 0.900 0.56 0.320 0.800 0.000 20210217 0.700 0.99 0.008 0.230 0.560 20210219 0.900 0.43 0.760 0.980 0.500

---

new_df.index = pd.to_datetime(new_df.index, format='%Y%m%d')

new_df

0 1 2 3 4 2021-01-13 0.000 0.05 0.000 0.000 0.433 2021-01-22 0.000 0.13 0.000 0.000 0.128 2021-01-25 0.056 0.00 0.043 0.062 0.160 2021-02-13 0.900 0.56 0.320 0.800 0.000 2021-02-17 0.700 0.99 0.008 0.230 0.560 2021-02-19 0.900 0.43 0.760 0.980 0.500

---

new_df = new_df.resample("10D").mean().dropna(how='all')

new_df

0 1 2 3 4 2021-01-13 0.000000 0.09 0.000000 0.000 0.280500 2021-01-23 0.056000 0.00 0.043000 0.062 0.160000 2021-02-12 0.833333 0.66 0.362667 0.670 0.353333

---

new_df.T

2021-01-13 2021-01-23 2021-02-12 0 0.0000 0.056 0.833333 1 0.0900 0.000 0.660000 2 0.0000 0.043 0.362667 3 0.0000 0.062 0.670000 4 0.2805 0.160 0.353333