从CSV文件导入数据
>>> import pandas as pd
>>> df = pd.read_csv('UN98.csv', index_col=0)
>>> df.head()
region tfr contraception educationMale ... economicActivityMale economicActivityFemale illiteracyMale illiteracyFemale
Afghanistan Asia 6.90 NaN NaN ... 87.5 7.2 52.800 85.00
Albania Europe 2.60 NaN NaN ... NaN NaN NaN NaN
Algeria Africa 3.81 52.0 11.1 ... 76.4 7.8 26.100 51.00
American.Samoa Asia NaN NaN NaN ... 58.8 42.4 0.264 0.36
Andorra Europe NaN NaN NaN ... NaN NaN NaN NaN
[5 rows x 13 columns]
注意index_col=0，表示用第一列作为index, 同时第一列不会出现在数据里面。'UN98.csv'可以在扣扣群630011153 144081101找到。
获取列名的方法
- df.columns
>>> df.columns
Index(['region', 'tfr', 'contraception', 'educationMale', 'educationFemale',
'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita',
'economicActivityMale', 'economicActivityFemale', 'illiteracyMale',
'illiteracyFemale'],
dtype='object')
>>> 'tfr' in df.columns
True
- keys()
>>> df.keys()
Index(['region', 'tfr', 'contraception', 'educationMale', 'educationFemale',
'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita',
'economicActivityMale', 'economicActivityFemale', 'illiteracyMale',
'illiteracyFemale'],
dtype='object')
- 通过迭代获取列名
>>> for col_name in df.columns:
... print(col_name)
...
region
tfr
contraception
educationMale
educationFemale
lifeMale
lifeFemale
infantMortality
GDPperCapita
economicActivityMale
economicActivityFemale
illiteracyMale
illiteracyFemale
- 通过获取列名为列表
>>> list(df.columns)
['region', 'tfr', 'contraception', 'educationMale', 'educationFemale', 'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita', 'economicActivityMale', 'economicActivityFemale', 'illiteracyMale', 'illiteracyFemale']
- tolist()转换列名为列表
>>> df.columns.values.tolist()
['region', 'tfr', 'contraception', 'educationMale', 'educationFemale', 'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita', 'economicActivityMale', 'economicActivityFemale', 'illiteracyMale', 'illiteracyFemale']
- sorted()可以获取字母排序的列名
>>> sorted(df)
['GDPperCapita', 'contraception', 'economicActivityFemale', 'economicActivityMale', 'educationFemale', 'educationMale', 'illiteracyFemale', 'illiteracyMale', 'infantMortality', 'lifeFemale', 'lifeMale', 'region', 'tfr']
参考资料
- python测试开发项目实战-目录
- python工具书籍下载-持续更新
- python 3.7极速入门教程 - 目录
- 讨论qq群630011153 144081101
- 原文地址
- 本文涉及的python测试开发库 谢谢点赞！
- 本文相关海量书籍下载
- https://www.listendata.com/2019/06/pandas-drop-columns-from-dataframe.html
- python数据分析数据科学中文英文工具书籍下载-持续更新
- https://www.marsja.se/how-to-get-the-column-names-from-a-pandas-dataframe-print-and-list/?utm_source=rss&utm_medium=rss&utm_campaign=how-to-get-the-column-names-from-a-pandas-dataframe-print-and-list
- https://pbpython.com/record-linking.html
- https://realpython.com/pandas-python-explore-dataset/
根据列名获取列值
>>> df['tfr'].values
array([6.9 , 2.6 , 3.81, nan, nan, 6.69, nan, 2.62, 1.7 , 1.89, 1.42,
2.3 , 1.95, 2.97, 3.14, 1.73, 1.4 , 1.62, 3.66, 5.83, 5.89, 4.36,
1.4 , 4.45, 2.17, 2.7 , 1.45, 6.57, 6.28, 4.5 , 5.3 , 1.61, 3.56,
4.95, 5.51, 2.44, 1.8 , 2.69, 5.51, 5.87, 3.5 , 2.95, 1.6 , 1.55,
2.31, 1.4 , 6.24, 1.82, 5.39, nan, 2.8 , 4.32, 3.1 , 3.4 , 3.09,
5.51, 5.34, 1.3 , 7. , 2.76, 1.83, 1.63, nan, 2.85, 5.4 , 5.2 ,
8. , 1.9 , 1.3 , 5.28, 1.38, nan, 2.1 , 3.04, 4.9 , 6.61, 5.42,
2.32, 4.6 , 4.3 , 1.32, 1.4 , 2.19, 3.07, 2.63, 4.77, 5.25, 1.8 ,
2.75, 1.19, 5.1 , 2.44, 1.48, 5.13, 2.3 , 4.85, 3.8 , 2.1 , 1.65,
2.77, 3.21, 6.69, 1.4 , 2.75, 4.86, 6.33, 5.92, 1.45, 1.5 , 1.76,
1.6 , 1.9 , 5.65, 6.69, 3.24, 6.8 , 6.6 , 2.1 , 4.49, 2. , 5.03,
2.28, 2.75, 5.6 , 1.8 , nan, 3.27, 3.1 , 6.06, 3.3 , 4.9 , 4.95,
1.55, 2.1 , 2.53, 2.02, 3.85, 7.1 , 5.97, 5.11, 1.88, 7.2 , 5.02,
3. , 2.63, 4.65, 4.17, 2.98, 3.62, 1.65, 1.48, 2.1 , 3.77, 2.1 ,
1.4 , 1.35, 6. , 2.63, 3.82, 3.8 , nan, nan, 5.9 , 5.62, 2.59,
6.06, 1.79, 1.5 , 1.3 , 4.98, 7. , 3.81, 1.22, 2.1 , 3.86, 4.61,
2.39, 4.46, 1.8 , 1.46, 4. , 3.93, 5.48, 1.74, 6.08, 4.02, 2.1 ,
2.92, 2.5 , 3.58, nan, 7.1 , 1.38, 3.46, 1.72, 1.96, 2.25, 3.48,
4.36, 2.98, 2.97, 3.03, 3.98, 7.6 , 1.8 , 5.49, 4.68])
>>> list(df['tfr'].values)
[6.9, 2.6, 3.81, nan, nan, 6.69, nan, 2.62, 1.7, 1.89, 1.42, 2.3, 1.95, 2.97, 3.14, 1.73, 1.4, 1.62, 3.66, 5.83, 5.89, 4.36, 1.4, 4.45, 2.17, 2.7, 1.45, 6.57, 6.28, 4.5, 5.3, 1.61, 3.56, 4.95, 5.51, 2.44, 1.8, 2.69, 5.51, 5.87, 3.5, 2.95, 1.6, 1.55, 2.31, 1.4, 6.24, 1.82, 5.39, nan, 2.8, 4.32, 3.1, 3.4, 3.09, 5.51, 5.34, 1.3, 7.0, 2.76, 1.83, 1.63, nan, 2.85, 5.4, 5.2, 8.0, 1.9, 1.3, 5.28, 1.38, nan, 2.1, 3.04, 4.9, 6.61, 5.42, 2.32, 4.6, 4.3, 1.32, 1.4, 2.19, 3.07, 2.63, 4.77, 5.25, 1.8, 2.75, 1.19, 5.1, 2.44, 1.48, 5.13, 2.3, 4.85, 3.8, 2.1, 1.65, 2.77, 3.21, 6.69, 1.4, 2.75, 4.86, 6.33, 5.92, 1.45, 1.5, 1.76, 1.6, 1.9, 5.65, 6.69, 3.24, 6.8, 6.6, 2.1, 4.49, 2.0, 5.03, 2.28, 2.75, 5.6, 1.8, nan, 3.27, 3.1, 6.06, 3.3, 4.9, 4.95, 1.55, 2.1, 2.53, 2.02, 3.85, 7.1, 5.97, 5.11, 1.88, 7.2, 5.02, 3.0, 2.63, 4.65, 4.17, 2.98, 3.62, 1.65, 1.48, 2.1, 3.77, 2.1, 1.4, 1.35, 6.0, 2.63, 3.82, 3.8, nan, nan, 5.9, 5.62, 2.59, 6.06, 1.79, 1.5, 1.3, 4.98, 7.0, 3.81, 1.22, 2.1, 3.86, 4.61, 2.39, 4.46, 1.8, 1.46, 4.0, 3.93, 5.48, 1.74, 6.08, 4.02, 2.1, 2.92, 2.5, 3.58, nan, 7.1, 1.38, 3.46, 1.72, 1.96, 2.25, 3.48, 4.36, 2.98, 2.97, 3.03, 3.98, 7.6, 1.8, 5.49, 4.68]
重命名列名
>>> df.rename(columns={'tfr': 'TFR'})
region TFR contraception educationMale ... economicActivityMale economicActivityFemale illiteracyMale illiteracyFemale
Afghanistan Asia 6.90 NaN NaN ... 87.5 7.2 52.800 85.000
Albania Europe 2.60 NaN NaN ... NaN NaN NaN NaN
Algeria Africa 3.81 52.0 11.1 ... 76.4 7.8 26.100 51.000
American.Samoa Asia NaN NaN NaN ... 58.8 42.4 0.264 0.360
Andorra Europe NaN NaN NaN ... NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ...
Western.Sahara Africa 3.98 NaN NaN ... NaN NaN NaN NaN
Yemen Asia 7.60 7.0 NaN ... 80.6 1.9 32.406 69.552
Yugoslavia Europe 1.80 NaN NaN ... NaN NaN 1.782 9.072
Zambia Africa 5.49 25.0 7.9 ... NaN NaN 14.400 28.700
Zimbabwe Africa 4.68 48.0 NaN ... 77.7 46.7 9.600 20.100
[207 rows x 13 columns]
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