def stream_half(col1, col2):
    if col1 == 'desktop':
        return col2/2
    else:
        return int(col2)
        
df['clean'] = df.apply(lambda row: stream_half(row['device'], 
  row['streams']), axis = 1)

Popularity 10/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: function functio

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Contributed on Mar 30 2022

Kat

0 Answers Avg Quality 2/10

pandas set condition multi columns

Comment

conditions = [
            (df['col2'] == 'Z') & (df['col1'] == 'A'),
            (df['col2'] == 'Z') & (df['col1'] == 'B'),
            (df['col1'] == 'B')
			]


choices = ['yellow', 'blue', 'purple']

df['color'] = np.select(conditions, choices, default='black')

df
# Out[216]: 
#  col1 col2   color
# 0    A    Z  yellow
# 1    B    Z    blue
# 2    B    X  purple
# 3    C    Y   black

Popularity 9/10 Helpfulness 4/10 Language python

Source: pandas.pydata.org

Tags: pandas pa

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Contributed on Apr 19 2022

0 Answers Avg Quality 2/10

apply on dataframe access multiple columns

Comment

df['col_3'] = df.apply(lambda x: x.col_1 + x.col_2, axis=1)

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: apply appl

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Contributed on Mar 22 2022

Tirbo06

0 Answers Avg Quality 2/10

pandas set condition multi columns

Comment

conditions = [
   .....:     (df['col2'] == 'Z') & (df['col1'] == 'A'),
   .....:     (df['col2'] == 'Z') & (df['col1'] == 'B'),
   .....:     (df['col1'] == 'B')
   .....: ]
   .....: 

In [214]: choices = ['yellow', 'blue', 'purple']

In [215]: df['color'] = np.select(conditions, choices, default='black')

In [216]: df
Out[216]: 
  col1 col2   color
0    A    Z  yellow
1    B    Z    blue
2    B    X  purple
3    C    Y   black

Popularity 9/10 Helpfulness 2/10 Language python

Source: pandas.pydata.org

Tags: pandas pa

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Contributed on Apr 19 2022

0 Answers Avg Quality 2/10

dataframe isin multiple columns

Comment

pd.merge(df2, df1, how='inner')
#         col1  col2
# 0      pizza   boy
# 1      pizza  girls
# 2  ice cream   boy

Popularity 9/10 Helpfulness 1/10 Language python

Source: stackoverflow.com

Tags: dataframe data

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Contributed on Aug 13 2021

Concerned Capybara

0 Answers Avg Quality 2/10

how to create multiple columns after applying a function in pandas column python

Comment

>>> df = pd.DataFrame([[i] for i in range(10)], columns=['num'])
>>> df
    num
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9

>>> def powers(x):
>>>     return x, x**2, x**3, x**4, x**5, x**6

>>> df['p1'], df['p2'], df['p3'], df['p4'], df['p5'], df['p6'] = \
>>>     zip(*df['num'].map(powers))

>>> df
        num     p1      p2      p3      p4      p5      p6
0       0       0       0       0       0       0       0
1       1       1       1       1       1       1       1
2       2       2       4       8       16      32      64
3       3       3       9       27      81      243     729
4       4       4       16      64      256     1024    4096
5       5       5       25      125     625     3125    15625
6       6       6       36      216     1296    7776    46656
7       7       7       49      343     2401    16807   117649
8       8       8       64      512     4096    32768   262144
9       9       9       81      729     6561    59049   531441

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Source: stackoverflow.com

Tags: function functio

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Contributed on Mar 30 2022

Darkstar

0 Answers Avg Quality 2/10

how to apply a function to multiple columns in pandas dataframe

Comment

-1

>>> print df
   A  B  C
0 -1  0  0
1 -4  3 -1
2 -1  0  2
3  0  3  2
4  1 -1  0
>>> print df.applymap(lambda x: x>1)
       A      B      C
0  False  False  False
1  False   True  False
2  False  False   True
3  False   True   True
4  False  False  False

Popularity 10/10 Helpfulness 1/10 Language python

Source: stackoverflow.com

Tags: apply appl

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Contributed on Oct 17 2021

Darkstar

0 Answers Avg Quality 2/10

pandas operations with multiple columns

Contents

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