Python select column on the left from another column-CodePudding

I have a tricky problem to select column in a dataframe. I have a dataframe and multiple columns in it have the same name "PTime".

This is my dataframe:

  PTime first_column  PTime third_column    PTime    fourth_column
0   4   first_value     1    first_value      6        first_value
1   4   second_value    2    second_value     7        second_value

This is what I want:

  PTime first_column       PTime    fourth_column
0   4    first_value        6        first_value
1   4    second_value       7        second_value

I will select my columns from a list:

My code:

data = {'PTime': ['1', '1'],
        'first_column':  ['first_value', 'second_value'],
        'PTime': ['2', '2'],
        'third_column': ['first_value', 'second_value'],
        'PTime': ['4', '4'],
         'fourth_column': ['first_value', 'second_value'],
        }
list_c = ['PTime','first_column','fourth_column']

df = pd.DataFrame(data)

#df = df[df.columns.intersection(list_c)]

df = df[list_c]
df

So my goal is to select the column that is in the list and select the column to the left of the one in the list. I if you have any idea to do that, thank you really much. Regards

CodePudding user response：

I don't exactly know how to get left of one in list But i have a trick to get desired table which you want as shown

   PTime first_column       PTime    fourth_column

0   4    first_value        6        first_value

1   4    second_value       7        second_value

what we can do is simply remove the column by index But here as there are same name pandas will to try to delete the first row But you can simply rename the columns if there are duplicates name and then you can use indexing to delete columns..

So here find some logic to rename it like PTime1 .. PTime2 .. PTime3 .. and then use indexes to remove it

df.drop(df.columns[i], axis=1,inplace=True)
                //  or //
df = df.drop(df.columns[i], axis=1)

Here you have to pass the list of indices . In your case it will be like

df.drop(df.columns[[2,3]],axis=1)

After renaming columns

CodePudding user response：

In my dataframe I will not have multiple columns with the same name. All names will be distinct.

So in the case I have ten columns to select it will be difficult to list them all in a list.

data = {'PTime1': ['1', '1'],
        'first_column':  ['first_value', 'second_value'],
        'PTime2': ['2', '2'],
        'third_column': ['first_value', 'second_value'],
        'PTime3': ['4', '4'],
         'fourth_column': ['first_value', 'second_value'],
        }
list_c = ['first_column','fourth_column']  #define column to select

df = pd.DataFrame(data)  #create dataframe

list_index = []  #create list to store index column
for col in list_c:   
    index_no = df.columns.get_loc(col)  #get index column
    list_index.append(index_no-1)  #insert index-1 in a list. Get column from the left
    list_index.append(index_no)  #insert index from the column in the list.

df = df.iloc[:, list_index]  #Subset the dataframe with the list of column selected.
df

Like this I can select the column from my list and the column on the left of each element in my list.