如何用 python 中的 for 循环覆盖列表中的 dataframe？

Question

Hello I want to modify my dataframes in a list with a for loop. My function works well and my dataframes are modified inside the function but once I want to have these new dataframes (with the same name as the old ones), it is not the dataframes that are shown but the old ones. I conclude that I can't overwrite my old dataframes.

All my dataframes in the list are of the form:

index  customer_region  number_order    distance_between_seller_customer    date_last_order mean_days_between_orders    mean_item_per_order mean_volume_item_ordered    
69  Southeast   1.0 1.850759    736411.0    0.0 
74  Southeast   1.0 0.250155    736404.0    0.0 
93  Northeast   1.0 20.223906   736416.0    0.0 
101 Southeast   1.0 0.989547    736366.0    0.0

Preparation

all_dfs = [sample_1strim, sample_2strim, sample_3strim]

function to normalized digital columns, encoded nominal column to digital columns and modify old dataframe per dataframe merge of previous modification:

def get_df_name(df):    #to get dataframe name
    name =[x for x in globals() if globals()[x] is df][0]
    return name

def standartization_encodage (frame):
  X = frame.copy()
  categorical_columns = X.select_dtypes(['category','object']).columns
  numerical_columns = X.select_dtypes(['int64','float64']).columns
  X[numerical_columns] = StandardScaler().fit_transform(X[numerical_columns])
  one_hot_encoded = pd.get_dummies(X[categorical_columns])
  X = pd.merge(X[numerical_columns], one_hot_encoded,left_index = True, right_index = True)
  X.shape
  X = X.set_index(frame.index)

  return X

            
for i, df in enumerate(all_dfs):
    all_dfs[i] = standartization_encodage(df)

Thanks in advance.

Answer 1

I would just use a function and apply it to all elements in the list of dataframes.

def transform_df(df):
    # your code in the loop
    return transformed_df

all_dfs = [transform_df(df) for df in all_dfs]

Answer 2

I may be wrong, but I think you have misunderstood how variables and lists work in Python. You have

df1 = (some dataframe)
all_dfs = [df1]

This means that you have a dataframe, and that the variable df1 , as well as the first list entry, hold references to that dataframe:

df1 -------> (ORIGINAL DATAFRAME)
               ^
               |
all_dfs ---> [ 0 ]

Then, you effectively do

df1_1 = all_dfs[0].copy()
df1_1 = (stuff that modifies df1_1)
all_dfs[0] = df1_1

This means that you have created a new dataframe, and changed the first entry of the list to refer to this new dataframe. However, the variable df1 still refers to the original dataframe!

df1 -------> (ORIGINAL DATAFRAME)

all_dfs ---> [ 0 ]
               |
               v
              (NEW DATAFRAME)

You cannot change a variable indirectly through a list as (I think) you are trying to do. You might be able to get the same effect if, instead of creating a new df, you can modify the existing one in place (unfortunately, I am not familiar enough with pandas to know off hand whether that is possible).