Pandas Format Pivot Table
I came across a problem in formatting a pivot table created by Pandas. So I made a matrix table between two columns (A, B) from my original data using Pandas.pivot_table with A as column and B as index.
>> df = PD.read_excel("data.xls")
>> table = PD.pivot_table(df,index=["B"],
values='Count',columns=["A"],aggfunc=[NUM.sum],
fill_value=0,margins=True,dropna= True)
>> table
It returns as:
sum
A 1 2 3 All
B
1 23 52 0 75
2 16 35 12 65
3 56 0 0 56
All 95 87 12 196
And I hope to have this format:
A All_B
1 2 3
1 23 52 0 75
B 2 16 35 12 65
3 56 0 0 56
All_A 95 87 12 196
How should I do it? Thank you very much forward.
+3
source to share
1 answer
The table returned pd.pivot_table
is very easy to work with (it is a sibling index / column) and usually does NOT require further format processing. But if you insist on changing the format to the one you mentioned in the post, you need to build a multi-level index / column using pd.MultiIndex
. Here's an example on how to do this.
Before manipulation
import pandas as pd
import numpy as np
np.random.seed(0)
a = np.random.randint(1, 4, 100)
b = np.random.randint(1, 4, 100)
df = pd.DataFrame(dict(A=a,B=b,Val=np.random.randint(1,100,100)))
table = pd.pivot_table(df, index='A', columns='B', values='Val', aggfunc=sum, fill_value=0, margins=True)
print(table)
B 1 2 3 All
A
1 454 649 770 1873
2 628 576 467 1671
3 376 247 481 1104
All 1458 1472 1718 4648
After:
multi_level_column = pd.MultiIndex.from_arrays([['A', 'A', 'A', 'All_B'], [1,2,3,'']])
multi_level_index = pd.MultiIndex.from_arrays([['B', 'B', 'B', 'All_A'], [1,2,3,'']])
table.index = multi_level_index
table.columns = multi_level_column
print(table)
A All_B
1 2 3
B 1 454 649 770 1873
2 628 576 467 1671
3 376 247 481 1104
All_A 1458 1472 1718 4648
+1
source to share