Duplicate entries and rename column row in pandas pivot table without aggregation

Question

I\'m trying to reshape this sample dataframe from long to wide format, without aggregating any of the data.

import numpy as np
import pandas as pd

df = pd.         


        

Answer 1

You are on the correct path:

# group
df['idx'] = df.groupby('Date').cumcount()

# set index and unstack
new = df.set_index(['idx','Date', 'SubjectID']).unstack(level=[0,1])

# drop idx column
new.columns = new.columns.droplevel(1)
new.columns = [f'{val}_{date}' for val, date in new.columns]

I think this is your expected output

Using map looks like it will be a little faster:

df['idx'] = df.groupby('Date').cumcount()
df['Date'] = df['Date'].astype(str)
new = df.set_index(['idx','Date', 'SubjectID']).unstack(level=[0,1])
new.columns = new.columns.droplevel(1)
#new.columns = [f'{val}_{date}' for val, date in new.columns]
new.columns = new.columns.map('_'.join)

Here is a 50,000 row test example:

#data
data = pd.DataFrame(pd.date_range('2000-01-01', periods=50000, freq='D'))
data['a'] = list('abcd')*12500
data['b'] = 2
data['c'] = list('ABCD')*12500
data.rename(columns={0:'date'}, inplace=True)

# list comprehension:
%%timeit -r 3 -n 200
new = data.set_index(['a','date','c']).unstack(level=[0,1])
new.columns = new.columns.droplevel(0)
new.columns = [f'{x}_{y}' for x,y in new.columns]

# 98.2 ms ± 13.3 ms per loop (mean ± std. dev. of 3 runs, 200 loops each)

# map with join:
%%timeit -r 3 -n 200
data['date'] = data['date'].astype(str)
new = data.set_index(['a','date','c']).unstack(level=[0,1])
new.columns = new.columns.droplevel(0)
new.columns = new.columns.map('_'.join)

# 84.6 ms ± 3.87 ms per loop (mean ± std. dev. of 3 runs, 200 loops each)