在 Python dataframe 中有什么方法可以查看两列是否相同但具有重命名的值？

Question

For example if I have a large dataframe of all individuals in a zoo and two columns are Animal_Common_Name and Animal_Scientific_Name. I suspect one of those is redundant as one characteristic is totally determined by the other and viceversa. Basically are the same charasteristic but renamed.

Is there any fuction that selected two different columns tell you so?

Answer 1

You can use the pandas.Series.equals() method.

For example:

import pandas as pd

data = {
    'Column1': [1, 2, 3, 4],
    'Column2': [1, 2, 3, 4],
    'Column3': [5, 6, 7, 8]
}

df = pd.DataFrame(data)

# True
print(df['Column1'].equals(df['Column2']))

# False
print(df['Column1'].equals(df['Column3']))

Found via GeeksForGeeks

Answer 2

df['Animal_Common_Name'].equals(df['Animal_Scientific_Name'])

This should return True if they're the same and false if not.

Answer 3

You can use the vectorized operations of pandas to quickly determine your redundancies. Here's an example:

import pandas as pd

# create a sample dataframe from some data
d = {'name1': ['Zebra', 'Lion', 'Seagull', 'Spider'],
     'name2': ['Zebra', 'Lion', 'Bird', 'Insect']}
df = pd.DataFrame(data=d)

# create a new column for your test:
df['is_redundant'] = ''

# select your empty column where the redundancy exists:
df['is_redundant'][df['name1']==df['name2']] = 1

print(df)


    name1   name2   is_redundant
0   Zebra   Zebra   1
1   Lion    Lion    1
2   Seagull Bird    
3   Spider  Insect  

You can then replace the empties with 0 or leave as is depending on your application.

Answer 4

Assuming this example:

  Animal_Common_Name  Animal_Scientific_Name
0               Lion            Panthera leo
1            Giraffe  Giraffa camelopardalis
2               Lion            Panthera leo

Use factorize to convert to a categorical integer, then compare is all values are equal:

(pd.factorize(df['Animal_Common_Name'])[0] == pd.factorize(df['Animal_Scientific_Name'])[0]).all()

Output: True

If you want to identify multiple relationships:

df[df.groupby('Animal_Scientific_Name')['Animal_Common_Name'].transform('nunique').ne(1)]

And the same with the column names swapped.