Using condition to split pandas column of lists into multiple columns.

Question

I have a pandas dataFrame with two columns that looks like the following:

d1 = {'Time1': [[93, 109, 187],[159],[94, 96, 154, 169]],
              'Time2':[[16, 48, 66, 128],[123, 136],[40,177,192]]}

df = pd.DataFrame(d1)

I need to split these columns of lists into 4 columns named 1st_half_T1, 2nd_half_T1, 1st_half_T2 and 2nd_half_T2 using pandas. The condition is, Time1 splits into 1st_half if Time <= 96 and 2nd_half if Time > 96 and applying the same condition to Time2 gives the following output.

  1st_half_T1           2nd_half_T1     1st_half_T2      2nd_half_T2
0       [93]             [109, 187]    [16, 48, 66]           [128]
1         []                  [159]              []      [123, 126]
2   [94, 96]             [154, 169]            [40]      [177, 192]

Dillon · Accepted Answer · 2018-06-25 10:32:28Z

2

Use apply with a custom function

def my_split(row):
    return pd.Series({
        '1st_half_T1': [i for i in row.Time1 if i <= 96],
        '2nd_half_T1': [i for i in row.Time1 if i > 96],
        '1st_half_T2': [i for i in row.Time2 if i <= 96],
        '2nd_half_T2': [i for i in row.Time2 if i > 96]
    })
df.apply(my_split, axis=1)

Out[]:
  1st_half_T1   1st_half_T2 2nd_half_T1 2nd_half_T2
0        [93]  [16, 48, 66]  [109, 187]       [128]
1          []            []       [159]  [123, 136]
2    [94, 96]          [40]  [154, 169]  [177, 192]

answered Jun 25, 2018 at 10:32

Dillon

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1 Comment

A.Abs Over a year ago

Thank you all for your help.

jezrael · Accepted Answer · 2018-06-25 10:31:06Z

2

Use list comprehensions with DataFrame constructor:

t11 = [[y for y in x if y <=96] for x in df['Time1']]
t12 = [[y for y in x if y >96] for x in df['Time1']]

t21 = [[y for y in x if y <=96] for x in df['Time2']]
t22 = [[y for y in x if y >96] for x in df['Time2']]

df = pd.DataFrame({'1st_half_T1':t11, '2nd_half_T1':t12,'1st_half_T2':t21, '2nd_half_T2':t22})
print (df)
  1st_half_T1 2nd_half_T1   1st_half_T2 2nd_half_T2
0        [93]  [109, 187]  [16, 48, 66]       [128]
1          []       [159]            []  [123, 136]
2    [94, 96]  [154, 169]          [40]  [177, 192]

answered Jun 25, 2018 at 10:31

jezrael

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Comments

theletz · Accepted Answer · 2018-06-25 10:33:02Z

2

df_new = pd.DataFrame()

df_new.loc[:,'1st_half_T1'] = df['Time1'].apply(lambda x : [y for y in x if y <=96])
df_new.loc[:,'2nd_half_T1'] = df['Time1'].apply(lambda x : [y for y in x if y >96])
df_new.loc[:,'1st_half_T2'] = df['Time2'].apply(lambda x : [y for y in x if y <=96])
df_new.loc[:,'2nd_half_T2'] = df['Time2'].apply(lambda x : [y for y in x if y >96])
df_new
Out[64]: 
  1st_half_T1 2nd_half_T1   1st_half_T2 2nd_half_T2
0        [93]  [109, 187]  [16, 48, 66]       [128]
1          []       [159]            []  [123, 136]
2    [94, 96]  [154, 169]          [40]  [177, 192]

answered Jun 25, 2018 at 10:33

theletz

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