Selecting multiple columns from pandas DataFrame based on column contents using very complex conditi-CodePudding

Does anybody know how to select multiple columns from a pandas DataFrame whose contents meet multiple complex conditions? For example, the columns I'm trying to select MUST contain at least one instance of the integers 1 through 4 and MAY contain np.nan but CANNOT contain any other integers (below 1 or above 4) or datatypes.

I know I can do this by executing many lines of code in a row, but I'm hoping to find a better solution (especially one that will catch outside cases).

Example:

# import packages
import pandas as pd
import numpy as np

# Initialize list of lists
data = [[1, 4, 3, 5], [2, np.nan, 1, 3], [4, 3, 2, 1], [3, 3, 1, 4]]
  
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])

print(df)

Output:

   A    B  C  D
0  1  4.0  3  5
1  2  NaN  1  3
2  4  3.0  2  1
3  3  3.0  1  4

Execute many statements or some optimized code to fulfill above conditions

df = df.select_dtypes(include='number')
df = df.loc[:,[(df[col] == 1).any() for col in df.columns]]
df = df.loc[:,[(df[col] == 2).any() for col in df.columns]]
df = df.loc[:,[(df[col] == 3).any() for col in df.columns]]
df = df.loc[:,[(df[col] == 4).any() for col in df.columns]]
df = df.loc[:,[~(df[col] > 4).any() for col in df.columns]]
df = df.loc[:,[~(df[col] < 1).any() for col in df.columns]]
print(df)

Output:

CodePudding user response：

import numpy as np
import pandas as pd
data = [[1, 4, 3, 5], [2, np.nan, 1, 3], [4, 3, 2, 1], [3, 3, 1, 4]]
df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])
mat = df.values
cols = (~((mat < 1) | (mat > 4))).all(axis=0) & np.in1d(mat, np.array(range(1,5))).reshape(mat.shape).any(axis=0)
print(df.loc[:,cols])

Output:

   A    B  C
0  1  4.0  3
1  2  NaN  1
2  4  3.0  2
3  3  3.0  1

CodePudding user response：

Combine them in one call and pass to loc:

(df
.select_dtypes('number')
.loc(axis=1)[lambda df: df.eq(1).any() & 
                        df.eq(2).any() & 
                        df.eq(3).any() & 
                        df.eq(4).any() & 
                        df.le(4).any() & 
                        df.ge(1).any()
             ]
)

   A
0  1
1  2
2  4
3  3

Taking it a bit further, let's encapsulate your logic with less steps:

trim = df.transform(lambda df: pd.Series([1,2,3,4]).isin(df)).all()
df.loc(axis=1)[trim]
   A
0  1
1  2
2  4
3  3