Vectorize your data

Pandas vectorized methods

Concepts covered

1. Lambda Refresher
   • lambda functions are small inline functions that are defined on-the-fly in Python
   • lambda x: x>= 1 will take an input x and return x>=1, or a boolean that equals True or False.
2. map()
   • create a new Series by applying the lambda function to each element
   • can only be used on a Series to return a new Series
3. applymap()
   • create a new DataFrame by applying the lambda function to each element
   • can only be used on a DataFrame to return a new DataFrame
4. df.apply(numpy.mean)
   • Get mean of every column in a DataFrame
   • Exactly the same as df.mean()

In [50]:

import pandas as pd
import numpy as np

In [51]:

# columns
columns = ['one', 'two']

In [52]:

# index
index = ['a', 'b', 'c', 'd']

In [53]:

# lists
one = [1, 2, 3, 4]
two = [1, 2, 3, 4]

In [54]:

# dictionary
d = {
    'one': one,
    'two': two
}

In [55]:

# DataFrame
df = pd.DataFrame(d, columns=col, index=index)

In [56]:

df

Out[56]:

	one	two
a	1	1
b	2	2
c	3	3
d	4	4

In [58]:

# mean of every single column in df
df.apply(np.mean)

Out[58]:

one    2.5
two    2.5
dtype: float64

In [61]:

# you can use a pandas command too
df.mean()

Out[61]:

one    2.5
two    2.5
dtype: float64

In [64]:

# .map() on particular columns (Series)
# goes through every value in column and evaluate if it's > 1
df['one'].map(lambda x: x >= 1)

Out[64]:

a    True
b    True
c    True
d    True
Name: one, dtype: bool

In [65]:

df.applymap(lambda x: x >= 1)

Out[65]:

	one	two
a	True	True
b	True	True
c	True	True
d	True	True

Tags: pandas