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df = pd.DataFrame({'col1':[0,1,2], 'col2':[0,1,2], 'col3':[0,1,2]})
df
col1 col2 col3
0 0 0 0
1 1 1 1
2 2 2 2
df['col1'], df['col2'], df['col3'] = zip(*df.apply(lambda r: (1, 2, 3), axis=1))
df
col1 col2 col3
0 1 2 3
1 1 2 3
2 1 2 3
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cols = ['foo', 'bar', 'new']
df['combined'] = df[cols].apply(lambda row: '_'.join(row.values.astype(str)), axis=1)
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import pandas as pd
df = {'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]}
df = pd.DataFrame(df)
df[[ 'column_new_1', 'column_new_2','column_new_3']] = [np.nan, 'dogs',3] #thought this wo
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df['column_new_1'], df['column_new_2'], df['column_new_3'] = [np.nan, 'dogs', 3]
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>>> 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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>>> 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
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df.stack().reset_index()
level_0 level_1 0
0 0 Column 1 A
1 0 Column 2 E
2 1 Column 1 B
3 1 Column 2 F
4 2 Column 1 C
5 2 Column 2 G
6 3 Column 1 D
7 3 Column 2 H
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In [1069]: df.assign(**{'col_new_1': np.nan, 'col2_new_2': 'dogs', 'col3_new_3': 3})
Out[1069]:
col_1 col_2 col2_new_2 col3_new_3 col_new_1
0 0 4 dogs 3 NaN
1 1 5 dogs 3 NaN
2 2 6 dogs 3 NaN
3 3 7 dogs 3 NaN