pandas dataframe get rows when list values in specific columns meet certain condition

Question

I have a dataframe:

df = A      B 
     1  [0.2,0.8]
     2  [0.6,0.9]

I want to get only rows where all the values of B are >= 0.5 So here:

new_df = A     B
         2 [0.6, 0.9]

What is the best way to do it?

Answer 1

You can use apply to filter the values,

import pandas as pd
df = pd.DataFrame({'A': [1,2], 'B':[[0.2, 0.8], [0.6, 0.9]]})
print(df[df['B'].apply(lambda x: all([i>=0.5 for i in x]))])

Answer 2

You can

1. explode the list in B column to rows
2. check if the rows are all greater and equal than 0.5 based on index group
3. boolean indexing the df with satisfied rows

out = df[df.explode('B')['B'].ge(0.5).groupby(level=0).all()]

print(out)

   A           B
1  2  [0.6, 0.9]

Answer 3

Method1:

first drive a new columns e.g. flag which indicate the result of filter condition. Then use this flag to filter out records. I am using a custom function to drive flag value. You can do much more operations in custom function. Below code:

def fun1(r):
    flg = all(b>0.5 for b in r['B'])
    #print(flg)
    r['flg'] = flg
    return r
    
df1 = pd.DataFrame([{'A':1,'B':[0.2,0.8]},{'A':2,'B':[0.6,0.9]}])

# 
df1 = df1[df1.apply(fun1, axis=1)['flg']==True]
df1

result:

A   B
2   [0.6, 0.9]

Method2:

Using lambda one liner:

df1 = df1[ df1['B'].apply(lambda x: all([b>0.5 for b in x])) ]

Answer 4

import pandas as pd

df = pd.DataFrame({'A':[1, 2],
                   'B':[[0.2,0.8], [0.6,0.9]],
})

mask = df.agg({'B': lambda v: all(map(lambda x: x>0.5, v))})

r = df[mask['B']]

print(r)

   A           B
1  2  [0.6, 0.9]