I have several dataframes where I need to reduce the dataframe to a time span for all of them. So that I don't have to reduce the codeblock over and over again, I would like to write a function.

Currently everything is realized without working by the following code:

timerange = (df_a['Date'].max() - pd.DateOffset(months=11))
df_a_12m = df_a.loc[df_a['Date'] >= timerange]

my approach:

def Time_range(Data_1, x,name, column, name):
   t = Data_1[column].max() - pd.DateOffset(months=x)
   'df'_   name = Data_1.loc[Data_1[column] >= t]

unfortunately this does not work

CodePudding user response：

There are a few mistakes in your approach. Firstly, when you create a new variable you need to specify exactly what it will be called. It is not possible to "dynamically" name a variable like you're trying with 'df_' name = something.

Second, variable scope dictates that any variable created in a function is only accessible inside that function, and ceases to exist once it finishes executing (unless you play special tricks with global variables). So, even if you did df_name = Data_1.loc[Data_1[column] >= t], once Time_range() finishes running, that variable will be deleted.

What you can do is have the function return the finished DataFrame and assign the result as a new variable from the outside:

def Time_range(Data_1, x, column):
    t = Data_1[column].max() - pd.DateOffset(months=x)
    return Data_1.loc[Data_1[column] >= t].copy()

df_any_name_you_want = Time_range(df_a, 11, 'Date')

Generally, this is what you want functions to do. Do some operations and return a finished value that you can then use from the outside.

CodePudding user response：

My approach would be:

1. Store your dataframes in a list e.g. dfs=[df_a,df_b]

2. Build a function from your approach. Input: (df, DeltaT=1, colName='Date'), Output: modified DataFrame

    def Time_range(df, DeltaT=1, colName='Date'): # Default Values for Delat T and colName. Helpful if constant in most of the cases.
       t = df[colName].max() - pd.DateOffset(months=DeltaT)
       df = df.loc[df[colName] >= t].copy() # Good advise to use copy() to ensure that you do not work on your original data by mistake. Espacially with the inplace=True argument you will increase the risk of un-expected behaviour 
       return df # Important: You have to return the result of your function
   

3. Call your function with your list

    result=[] #list for modified dfs
    for df in dfs:
        results.append(Time_range(df, DeltaT=2))
   

Important code was not tested. Might contain typos

Edit Formatting

Edit 2 Due to the discussion on my comment on the copy() command a small example with proper formatting:

import pandas as pd

def EmptyDataFrameInplace(df):
    df.drop('A', axis=1, inplace=True)

def EmptyDataFrame(df):
    df=df.drop('A', axis=1)

dfA=pd.DataFrame({'A':[1,2,3], 'B':[4,5,6]})
dfB=dfA.copy()
print(dfA.head())

EmptyDataFrameInplace(dfA)
EmptyDataFrame(dfB)

print(dfA.head())
print(dfB.head())

The result looks like this:

   A  B
0  1  4
1  2  5
2  3  6
   B
0  4
1  5
2  6
   A  B
0  1  4
1  2  5
2  3  6

Also see here Thus, I try always to use copy() to ensure that I don't modifiy a dataframe without notice.