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Python error – Length of passed values is 6, index implies 2 (while doing PIVOT...

 3 years ago
source link: https://sqlwithmanoj.com/2021/04/14/python-error-length-of-passed-values-is-6-index-implies-2-while-doing-pivot-with-multiindex-or-multiple-columns/
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Home > Python > Python error – Length of passed values is 6, index implies 2 (while doing PIVOT with MultiIndex or multiple columns)

Python error – Length of passed values is 6, index implies 2 (while doing PIVOT with MultiIndex or multiple columns)

As I’m new to Python and these days using it for some Data Analysis & Metadata handling purpose, and also being from SQL background here I’m trying to use as many analysis features I use with SQL, like Group By, Aggregate functions, Filtering, Pivot, etc.

Now I had this particular requirement to PIVOT some columns based on multi-index keys, or multiple columns as shown below:

Python PIVOT

But while using PIVOT function in Python I was getting this weird error that I was not able to understand, because this error was not coming with the use of single index column.

ValueError: Length of passed values is 6, index implies 2.

Let’s create a sample Pandas DataFrame for our demo purpose:

import pandas as pd
sampleData = {
'Country': ['India', 'India', 'India','India','USA','USA'],
'State': ['UP','UP','TS','TS','CO','CO'],
'CaseStatus': ['Active','Closed','Active','Closed','Active','Closed'],
'Cases': [100, 150, 300, 400, 200, 300]}
df = pd.DataFrame(sampleData)
df

Problem/Issue:

But when I tried using the below code which uses pivot() function with multiple columns or multi-indexes it started throwing error. I was not getting error when I used single index/column below. So what could be the reason?

# Passing multi-index (or multiple columns) fails here:
df_pivot = df.pivot(index = ['Country','State'],
columns = 'CaseStatus',
values = 'Cases').reset_index()
df_pivot

Solution #1

Online checking I found that the pivot() function only accepts single column index key (do not accept multiple columns list as index). So, in this case first we would need to use set_index() function and set the list of columns as shown below:

# Use pivot() function with set_index() function:
df_pivot = (df.set_index(['Country', 'State'])
.pivot(columns='CaseStatus')['Cases']
.reset_index()
)
df_pivot

Solution #2

There is one more simple option where you can use pivot_table() function as shown below and get the desired output:

# or use pivot_table() function:
df_pivot = pd.pivot_table(df,
index = ['Country', 'State'],
columns = 'CaseStatus',
values = 'Cases')
df_pivot

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