import pandas as pd
from functools import reduce
a = [1,2,3,4,5]
list(map(lambda x: x**2, a))
[1, 4, 9, 16, 25]
list(filter(lambda x: x >= 4, a))
[4, 5]
reduce(lambda x,y: x*y, a)
120
b = range(1000)
%timeit list(map(lambda x: x**2, b))
1000 loops, best of 3: 579 µs per loop
%timeit [x**2 for x in b]
1000 loops, best of 3: 468 µs per loop
keys = [l for l in 'abcdefghei']
keys
['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'e', 'i']
val = [v for v, _ in enumerate(keys)]
val
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
df = pd.DataFrame({'keys': keys, 'values': val})
df
| keys | values | |
|---|---|---|
| 0 | a | 0 |
| 1 | b | 1 |
| 2 | c | 2 |
| 3 | d | 3 |
| 4 | e | 4 |
| 5 | f | 5 |
| 6 | g | 6 |
| 7 | h | 7 |
| 8 | e | 8 |
| 9 | i | 9 |
Apply function element-wise to the whole DataFrame: the function is applied on an element-by-element basis !!!
df.applymap(str)
| keys | values | |
|---|---|---|
| 0 | a | 0 |
| 1 | b | 1 |
| 2 | c | 2 |
| 3 | d | 3 |
| 4 | e | 4 |
| 5 | f | 5 |
| 6 | g | 6 |
| 7 | h | 7 |
| 8 | e | 8 |
| 9 | i | 9 |
Apply function to DataFrame column-wise: function is applied to a column as a whole !!!
df.apply(lambda x: min(x), axis=0)
keys a values 0 dtype: object
Apply function to DataFrame row-wise: function is applied to a row as a whole !!!
df.apply(lambda x: ' '.join([str(el) for el in x]), axis=1)
0 a 0 1 b 1 2 c 2 3 d 3 4 e 4 5 f 5 6 g 6 7 h 7 8 e 8 9 i 9 dtype: object
df.apply?
Apply function to a DataSeries element-wise
df['values'].map(lambda x: x*10)
0 0 1 10 2 20 3 30 4 40 5 50 6 60 7 70 8 80 9 90 Name: values, dtype: int64
In summary, for Pandas:
- to apply function to the whole DataFrame element-wise: df.applymap(func)
- to apply function column- or row-wise: df.apply(func, axis)
- to apply function to a DataSeries element-wise: df['name'].map(func)
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