Python Pandas动态创建数据框 [英] Python Pandas Dynamically Create a Dataframe

问题描述

下面的代码将在 ONE 数据帧中生成所需的输出，但是，我想在FOR循环中动态创建数据帧，然后将移位后的值分配给该数据帧.例如，数据帧df_lag_12仅包含column1_t12和column2_12.任何想法将不胜感激.我试图使用EXEC语句动态创建12个数据框，谷歌搜索似乎表明这是一种不好的做法.

The code below will generate the desired output in ONE dataframe, however, I would like to dynamically create data frames in a FOR loop then assign the shifted value to that data frame. Example, data frame df_lag_12 would only contain column1_t12 and column2_12. Any ideas would be greatly appreciated. I attempted to dynamically create 12 dataframes using the EXEC statement, google searching seems to state this is poor practice.

import pandas as pd
list1=list(range(0,20))
list2=list(range(19,-1,-1))
d={'column1':list(range(0,20)),
   'column2':list(range(19,-1,-1))}
df=pd.DataFrame(d)
df_lags=pd.DataFrame()
for col in df.columns:
    for i in range(12,0,-1):
        df_lags[col+'_t'+str(i)]=df[col].shift(i)
    df_lags[col]=df[col].values  
print(df_lags)
for df in (range(12,0,-1)):
    exec('model_data_lag_'+str(df)+'=pd.DataFrame()')

动态创建的数据框DF_LAGS_12的所需输出:

var_list=['column1_t12','column2_t12']
df_lags_12=df_lags[var_list]  
print(df_lags_12)

推荐答案

我认为最好是创建dictionary of DataFrames:

d = {}
for i in range(12,0,-1):
    d['t' + str(i)] = df.shift(i).add_suffix('_t' + str(i))

如果需要先指定列:

d = {}
cols = ['column1','column2']
for i in range(12,0,-1):
    d['t' + str(i)] = df[cols].shift(i).add_suffix('_t' + str(i))

dict comprehension解决方案:

d = {'t' + str(i): df.shift(i).add_suffix('_t' + str(i)) for i in range(12,0,-1)}

---

print (d['t10'])
    column1_t10  column2_t10
0           NaN          NaN
1           NaN          NaN
2           NaN          NaN
3           NaN          NaN
4           NaN          NaN
5           NaN          NaN
6           NaN          NaN
7           NaN          NaN
8           NaN          NaN
9           NaN          NaN
10          0.0         19.0
11          1.0         18.0
12          2.0         17.0
13          3.0         16.0
14          4.0         15.0
15          5.0         14.0
16          6.0         13.0
17          7.0         12.0
18          8.0         11.0
19          9.0         10.0

全局变量是否可能，但是dictionary更好:

Is it possible by globals, but much better is dictionary:

d = {}
cols = ['column1','column2']
for i in range(12,0,-1):
    globals()['df' + str(i)] =  df[cols].shift(i).add_suffix('_t' + str(i))

print (df10)
    column1_t10  column2_t10
0           NaN          NaN
1           NaN          NaN
2           NaN          NaN
3           NaN          NaN
4           NaN          NaN
5           NaN          NaN
6           NaN          NaN
7           NaN          NaN
8           NaN          NaN
9           NaN          NaN
10          0.0         19.0
11          1.0         18.0
12          2.0         17.0
13          3.0         16.0
14          4.0         15.0
15          5.0         14.0
16          6.0         13.0
17          7.0         12.0
18          8.0         11.0
19          9.0         10.0

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