Pandas，第1部分

Python 中级

Hugo Bowne-Anderson

Data Scientist at DataCamp

表格数据集示例

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表格数据集示例

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表格数据集示例

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Python 中的数据集

二维 NumPy 数组？
- 单一数据类型

Python 中的数据集

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Python 中的数据集

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pandas！
- 高层数据处理工具
- Wes McKinney
- 基于 NumPy
- DataFrame

DataFrame

brics

         country    capital    area  population
BR        Brazil   Brasilia   8.516      200.40
RU        Russia     Moscow  17.100      143.50
IN         India  New Delhi   3.286     1252.00
CH         China    Beijing   9.597     1357.00
SA  South Africa   Pretoria   1.221       52.98

由字典创建 DataFrame

dict = { 
    "country":["Brazil", "Russia", "India", "China", "South Africa"],
    "capital":["Brasilia", "Moscow", "New Delhi", "Beijing", "Pretoria"],
       "area":[8.516, 17.10, 3.286, 9.597, 1.221]
 "population":[200.4, 143.5, 1252, 1357, 52.98] }

键（列标签）
值（按列的数据）

import pandas as pd

brics = pd.DataFrame(dict)

由字典创建 DataFrame（2）

brics

     area    capital       country  population
0   8.516   Brasilia        Brazil      200.40
1  17.100     Moscow        Russia      143.50
2   3.286  New Delhi         India     1252.00
3   9.597    Beijing         China     1357.00
4   1.221   Pretoria  South Africa       52.98

brics.index = ["BR", "RU", "IN", "CH", "SA"]

brics

      area    capital       country  population
BR   8.516   Brasilia        Brazil      200.40
RU  17.100     Moscow        Russia      143.50
IN   3.286  New Delhi         India     1252.00
CH   9.597    Beijing         China     1357.00
SA   1.221   Pretoria  South Africa       52.98

从 CSV 文件创建 DataFrame

brics.csv

,country,capital,area,population
BR,Brazil,Brasilia,8.516,200.4
RU,Russia,Moscow,17.10,143.5
IN,India,New Delhi,3.286,1252
CH,China,Beijing,9.597,1357
SA,South Africa,Pretoria,1.221,52.98

CSV = 逗号分隔值

从 CSV 文件创建 DataFrame

brics.csv

,country,capital,area,population
BR,Brazil,Brasilia,8.516,200.4
RU,Russia,Moscow,17.10,143.5
IN,India,New Delhi,3.286,1252
CH,China,Beijing,9.597,1357
SA,South Africa,Pretoria,1.221,52.98

brics = pd.read_csv("path/to/brics.csv")

brics

  Unnamed: 0       country    capital    area  population
0         BR        Brazil   Brasilia   8.516      200.40
1         RU        Russia     Moscow  17.100      143.50
2         IN         India  New Delhi   3.286     1252.00
3         CH         China    Beijing   9.597     1357.00
4         SA  South Africa   Pretoria   1.221       52.98

从 CSV 文件创建 DataFrame

brics = pd.read_csv("path/to/brics.csv", index_col = 0)

brics

         country  population      area    capital
BR        Brazil         200   8515767   Brasilia
RU        Russia         144  17098242     Moscow
IN         India        1252   3287590  New Delhi
CH         China        1357   9596961    Beijing
SA  South Africa          55   1221037   Pretoria

开始练习！

Python 中级