Advanced Pivot Table in Pandas

Question

I am trying to optimize some table transformation scripts in Python Pandas, which I am trying to feed with huge data sets (above 50k rows). I wrote a script that iterates through every index and parses values into a new data frame (see example below), but I am experiencing performance issues. Is there any pandas function, that could get the same results without iterating?

Example code:

from datetime import datetime
import pandas as pd

date1 = datetime(2019,1,1)
date2 = datetime(2019,1,2)

df = pd.DataFrame({"ID": [1,1,2,2,3,3],
                  "date": [date1,date2,date1,date2,date1,date2],
                  "x": [1,2,3,4,5,6],
                  "y": ["a","a","b","b","c","c"]})


new_df = pd.DataFrame()
for i in df.index:

    new_df.at[df.at[i, "ID"], "y"] = df.at[i, "y"]

    if df.at[i, "date"] == datetime(2019,1,1):
        new_df.at[df.at[i, "ID"], "x1"] = df.at[i, "x"]
    elif df.at[i, "date"] == datetime(2019,1,2):
        new_df.at[df.at[i, "ID"], "x2"] = df.at[i, "x"]

output:

   ID       date  x  y
0   1 2019-01-01  1  a
1   1 2019-01-02  2  a
2   2 2019-01-01  3  b
3   2 2019-01-02  4  b
4   3 2019-01-01  5  c
5   3 2019-01-02  6  c

   y   x1   x2
1  a  1.0  2.0
2  b  3.0  4.0
3  c  5.0  6.0

The transformation basically groups the rows by the "ID" column and gets the "x1" values from the rows with date 2019-01-01, and the "x2" values from the rows with date 2019-01-02. The "y" value is the same within the same "ID". "ID" columns become the new indexes.

I'd appreciate any advice on this matter.

Answer 1

Using pivot_tables will get what you are looking for:

result = df.pivot_table(index=['ID', 'y'], columns='date', values='x')
result.rename(columns={date1: 'x1', date2: 'x2'}).reset_index('y')