在 Python 中，有没有一种简单的方法可以以这种方式操作 dataframe？

Question

Hello, I have a dataframe that looks like 1 and would like to make it to 2. What would be the easiest way to do this in Python? Thank you

Answer 1

You can use for-loop to work with every column separatelly and later add this to new DataFrame

First get columns Code , State1_data ,
Next get string State1 from string State1_data and create column State with this value. Next rename column State1_data to data . Finally append this to new DataFrame .

And repeate it for other colums.

import pandas as pd

data = {
    'Code': [1,2,3],
    'State1_data': ['A','B','C'], 
    'State2_data': ['D','E','F'], 
    'State3_data': ['G','H','I'],
} # columns

df = pd.DataFrame(data)
print(df)

# create new DataFrame for result
new_df = pd.DataFrame()

# get names which start with `State`
#columns = ['State1_data', 'State2_data', 'State3_data']
columns = [name for name in df.columns if name.startswith('State')]

for col in columns:
    # get columns `Code`, `State1_data`
    temp_df = df[['Code', col]].copy()  

    # add value `State1` to column `State`
    temp_df['State'] = col.split('_')[0]            

    # rename `State1_data` to `data`
    temp_df = temp_df.rename(columns={col: 'data'})

    # append to new DataFrame
    new_df = new_df.append(temp_df)

# ---     

# change order
new_df = new_df[ ['Code', 'State', 'data'] ]  

# reset index
new_df = new_df.reset_index(drop=True)

print(new_df)

Result:

   Code State1_data State2_data State3_data
0     1           A           D           G
1     2           B           E           H
2     3           C           F           I
   Code   State data
0     1  State1    A
1     2  State1    B
2     3  State1    C
3     1  State2    D
4     2  State2    E
5     3  State2    F
6     1  State3    G
7     2  State3    H
8     3  State3    I

---

EDIT:

Now I see you use Code as index - so it needs little changes

import pandas as pd

data = {
    'Code': [1,2,3],
    'State1_data': ['A','B','C'], 
    'State2_data': ['D','E','F'], 
    'State3_data': ['G','H','I'],
} # columns

df = pd.DataFrame(data)
df.index = df['Code']
del df['Code']
print(df)

# create new DataFrame for result
new_df = pd.DataFrame()

# get names which start with `State`
#columns = ['State1_data', 'State2_data', 'State3_data']
#columns = [name for name in df.columns if name.startswith('State')]
columns = df.columns

for col in columns:
    # get columns `Code`, `State1_data`
    temp_df = df[[col]].copy()  

    # add value `State1` to column `State`
    temp_df['State'] = col.split('_')[0]            

    # rename `State1_data` to `data`
    temp_df = temp_df.rename(columns={col: 'data'})

    # append to new DataFrame
    new_df = new_df.append(temp_df)
    
# ---

# change order
new_df = new_df[ ['State', 'data'] ]  

print(new_df)

Result:

     State1_data State2_data State3_data
Code                                    
1              A           D           G
2              B           E           H
3              C           F           I
       State data
Code             
1     State1    A
2     State1    B
3     State1    C
1     State2    D
2     State2    E
3     State2    F
1     State3    G
2     State3    H
3     State3    I