Pivot large table in pandas

Question

I have a table like this:

df2 = pd.DataFrame(np.array([[1, 0, 3,0.2], [1, 100, 6,0.3], [7, 0, 9,0.7],[7, 100, 20,0.8]]),
                   columns=['id', 'time', 'val1','val2'])

        id  time    val1    val2
0      1.0  0.0     3.0     0.2
1      1.0  100.0   6.0     0.3
2      7.0  0.0     9.0     0.7
3      7.0  100.0   20.0    0.8

And i want to pivot (long table to wide table) and get something like this:

        id  val1time0   val2time0   val1time100 val2time100
0       1.0 3.0         0.2         6.0         0.3
2       7.0 9.0         0.7         20.0        0.8

My attempt was doing something like this:

df2=df2.merge(df2[df2["time"]==0][["id","val1","val2"]],left_on='id', right_on='id',suffixes=("", "time0"))
df2=df2.merge(df2[df2["time"]==100][["id","val1","val2"]],left_on='id', right_on='id',suffixes=("", "time100"))
df3=df2[["id","val1time0","val2time0","val1time100","val2time100"]]
df3.drop_duplicates()

And it works but when i have a large table, more than 80000 values in the first table, my kernel die, so can you help me?

Thank you

Answer 1

Hope this will help:

import pandas as pd
import numpy as np

df2 = pd.DataFrame(np.array([[1, 0, 3,0.2], [1, 100, 6,0.3], [7, 0, 9,0.7],[7, 100, 20,0.8]]),
                   columns=['id', 'time', 'val1','val2'])

df2["time"] = "time"+df2["time"].astype(str)

df3 = (df2.set_index(["id"])
                          .pivot(columns='time')[['val1', 'val2']]
                          .reset_index())

df3.columns = df3.columns.to_flat_index()

Output:

index	id	val1time0.0	val1time100.0	val2time0.0	val2time100.0
0	1.0	3.0	6.0	0.2	0.3
1	7.0	9.0	20.0	0.7	0.8