Wide to long returns empty output - Python dataframe

Question

I have a dataframe which can be generated from the code as given below

df = pd.DataFrame({'person_id' :[1,2,3],'date1': 
['12/31/2007','11/25/2009','10/06/2005'],'val1': 
[2,4,6],'date2': ['12/31/2017','11/25/2019','10/06/2015'],'val2':[1,3,5],'date3': 
['12/31/2027','11/25/2029','10/06/2025'],'val3':[7,9,11]})

I followed the below solution to convert it from wide to long

pd.wide_to_long(df, stubnames=['date', 'val'], i='person_id', 
j='grp').sort_index(level=0)

Though this works with sample data as shown below, it doesn't work with my real data which has more than 200 columns. Instead of person_id, my real data has subject_ID which is values like DC0001,DC0002 etc. Does "I" always have to be numeric? Instead it adds the stub values as new columns in my dataset and has zero rows

This is how my real columns looks like

My real data might contains NA's as well. So do I have to fill them with default values for wide_to_long to work?

Can you please help as to what can be the issue? Or any other approach to achieve the same result is also helpful.

Answer 1

The issue is with your column names, the numbers used to convert from wide to long need to be at the end of your column names or you need to specify a suffix to groupby. I think the easiest solution is to create a function that accepts regex and the dataframe.

import pandas as pd
import re

def change_names(df, regex):
    # Select one of three column groups
    old_cols = df.filter(regex = regex).columns
    # Create list of new column names
    new_cols = []
    for col in old_cols:
        # Get the stubname of the original column
        stub = ''.join(re.split(r'\d', col))
        # Get the time point
        num = re.findall(r'\d+', col) # returns a list like ['1']
        # Make new column name
        new_col = stub + num[0]
        new_cols.append(new_col)

    # Create dictionary mapping old column names to new column names
    dd = {oc: nc for oc, nc in zip(old_cols, new_cols)}
    # Rename columns
    df.rename(columns = dd, inplace = True)

    return df


tdf = pd.DataFrame({'person_id' :[1,2,3],'h1date': ['12/31/2007','11/25/2009','10/06/2005'],'t1val': [2,4,6],'h2date': ['12/31/2017','11/25/2019','10/06/2015'],'t2val':[1,3,5],'h3date': ['12/31/2027','11/25/2029','10/06/2025'],'t3val':[7,9,11]})

# Change date columns
tdf = change_names(tdf, 'date$')
tdf = change_names(tdf, 'val$')

print(tdf)
    person_id      hdate1  tval1      hdate2  tval2      hdate3  tval3
0          1  12/31/2007      2  12/31/2017      1  12/31/2027      7
1          2  11/25/2009      4  11/25/2019      3  11/25/2029      9
2          3  10/06/2005      6  10/06/2015      5  10/06/2025     11

Answer 2

尝试在允许字符串后缀的函数中添加其他参数。

pd.long_to_wide(.......................,suffix='\w+')

Answer 3

This is quite late to answer this question. But putting the solution here in case someone else find it useful

    tdf = pd.DataFrame({'person_id' :[1,2,3],'h1date': ['12/31/2007','11/25/2009','10/06/2005'],'t1val': [2,4,6],'h2date': ['12/31/2017','11/25/2019','10/06/2015'],'t2val':[1,3,5],'h3date': ['12/31/2027','11/25/2029','10/06/2025'],'t3val':[7,9,11]})
    
    ## You can use m13op22 solution to rename your columns with numeric part at the 
    ## end of the column name. This is important.
    
    tdf = tdf.rename(columns={'h1date': 'hdate1', 't1val': 'tval1',
                              'h2date': 'hdate2', 't2val': 'tval2',
                              'h3date': 'hdate3', 't3val': 'tval3'})

   ## Then use the non-numeric portion,  (in this example 'hdate', 'tval') as 
   ## stubnames. The mistake you were doing was using ['date', 'val'] as stubnames.

    df = pd.wide_to_long(tdf, stubnames=['hdate', 'tval'], i='person_id', j='grp').sort_index(level=0)
    
    print(df)