Pandas文摘：Applying Operations Over pandas Dataframes

2024-01-13 12:33:58

原文地址：https://chrisalbon.com/python/data_wrangling/pandas_apply_operations_to_dataframes/

Applying Operations Over pandas Dataframes

20 Dec 2017

Import Modules

import pandas as pd
import numpy as np

Create a dataframe

data = {‘name‘: [‘Jason‘, ‘Molly‘, ‘Tina‘, ‘Jake‘, ‘Amy‘], 
        ‘year‘: [2012, 2012, 2013, 2014, 2014], 
        ‘reports‘: [4, 24, 31, 2, 3],
        ‘coverage‘: [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, index = [‘Cochice‘, ‘Pima‘, ‘Santa Cruz‘, ‘Maricopa‘, ‘Yuma‘])
df

	coverage	name	reports	year
Cochice	25	Jason	4	2012
Pima	94	Molly	24	2012
Santa Cruz	57	Tina	31	2013
Maricopa	62	Jake	2	2014
Yuma	70	Amy	3	2014

Create a capitalization lambda function

capitalizer = lambda x: x.upper()

Apply the capitalizer function over the column ‘name’

apply() can apply a function along any axis of the dataframe

df[‘name‘].apply(capitalizer)

Cochice       JASON
Pima          MOLLY
Santa Cruz     TINA
Maricopa       JAKE
Yuma            AMY
Name: name, dtype: object

Map the capitalizer lambda function over each element in the series ‘name’

map() applies an operation over each element of a series

df[‘name‘].map(capitalizer)

Cochice       JASON
Pima          MOLLY
Santa Cruz     TINA
Maricopa       JAKE
Yuma            AMY
Name: name, dtype: object

Apply a square root function to every single cell in the whole data frame

applymap() applies a function to every single element in the entire dataframe.

# Drop the string variable so that applymap() can run
df = df.drop(‘name‘, axis=1)

# Return the square root of every cell in the dataframe
df.applymap(np.sqrt)

	coverage	reports	year
Cochice	5.000000	2.000000	44.855323
Pima	9.695360	4.898979	44.855323
Santa Cruz	7.549834	5.567764	44.866469
Maricopa	7.874008	1.414214	44.877611
Yuma	8.366600	1.732051	44.877611

Applying A Function Over A Dataframe

Create a function that multiplies all non-strings by 100

# create a function called times100
def times100(x):
    # that, if x is a string,
    if type(x) is str:
        # just returns it untouched
        return x
    # but, if not, return it multiplied by 100
    elif x:
        return 100 * x
    # and leave everything else
    else:
        return

Apply the times100 over every cell in the dataframe

df.applymap(times100)

	coverage	reports	year
Cochice	2500	400	201200
Pima	9400	2400	201200
Santa Cruz	5700	3100	201300
Maricopa	6200	200	201400
Yuma	7000	300	201400