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Healing: Can best evaluate identically-labeled line gadgets

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One error it’s possible you’ll come upon when the use of pandas is:

ValueError: Can best evaluate identically-labeled DataFrame gadgets

This mistake happens whilst you aim to match two pandas DataFrames and both the index labels or the column labels don’t completely fit.

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Refer to instance presentations cure this mistake in apply.

Reproduce the Error

Assume now we have please see two pandas DataFrames:

import pandas as pd

#outline DataFrames
df1 = pd.DataFrame({'issues': [25, 12, 15, 14],
                   'assists': [5, 7, 13, 12]})

df2 = pd.DataFrame({'issues': [25, 12, 15, 14],
                    'assists': [5, 7, 13, 12]},
                     index=[3, 2, 1, 0])

#view DataFrames
print(df1)

   issues  assists
0      25        5
1      12        7
2      15       13
3      14       12

print(df2)

   issues  assists
3      25        5
2      12        7
1      15       13
0      14       12

Realize that the column labels fit, however the index labels don’t.

If we aim to match the 2 DataFrames, we’ll obtain an error:

#aim to match the DataFrames
df1 = df2

ValueError: Can best evaluate identically-labeled DataFrame gadgets

Healing the Error

There are a couple of forms we will worth to deal with this mistake.

Mode 1: Examine DataFrames (together with index labels)

We will worth please see syntax to match the 2 DataFrames to peer in the event that they completely fit (together with the index labels):

df1.equals(df2)

Fraudelant

This tells us that the 2 DataFrames don’t completely fit (together with the index labels).

Mode 2: Examine DataFrames (forget about index labels)

We will worth please see syntax to match the 2 DataFrames to peer in the event that they completely fit, occasion totally ignoring the index labels:

df1.reset_index(leave=True).equals(df2.reset_index(leave=True))

True

This tells us that the 2 DataFrames completely fit (now not accounting for the index labels).

Mode 3: Examine DataFrames Row through Row

We will worth please see syntax to match the 2 DataFrames row through row to peer which row values fit:

df1.reset_index(leave=True) == df2.reset_index(leave=True)

      issues	assists
0	True	   True
1	True	   True
2	True	   True
3	True	   True

This permits us to peer which values fit in each and every row.

Backup Sources

Refer to tutorials give an explanation for cure alternative ordinary mistakes in Python:

Healing KeyError in Pandas
Healing: ValueError: can not convert flow NaN to integer
Healing: ValueError: operands may now not be broadcast at the side of shapes

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