6种从Pandas 数据帧(DataFrame)中获取列名的方法

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  illiteracyFemaleAfghanistan       Asia  6.90            NaN            NaN  ...                  87.5                     7.2          52.800             85.00Albania         Europe  2.60            NaN            NaN  ...                   NaN                     NaN             NaN               NaNAlgeria         Africa  3.81           52.0           11.1  ...                  76.4                     7.8          26.100             51.00American.Samoa    Asia   NaN            NaN            NaN  ...                  58.8                    42.4           0.264              0.36Andorra         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.columnsIndex(['region', 'tfr', 'contraception', 'educationMale', 'educationFemale',       'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita',       'economicActivityMale', 'economicActivityFemale', 'illiteracyMale',       'illiteracyFemale'],      dtype='object')>>> 'tfr' in df.columnsTrue

• 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)... regiontfrcontraceptioneducationMaleeducationFemalelifeMalelifeFemaleinfantMortalityGDPperCapitaeconomicActivityMaleeconomicActivityFemaleilliteracyMaleilliteracyFemale

• 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'].valuesarray([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  illiteracyFemaleAfghanistan       Asia  6.90            NaN            NaN  ...                  87.5                     7.2          52.800            85.000Albania         Europe  2.60            NaN            NaN  ...                   NaN                     NaN             NaN               NaNAlgeria         Africa  3.81           52.0           11.1  ...                  76.4                     7.8          26.100            51.000American.Samoa    Asia   NaN            NaN            NaN  ...                  58.8                    42.4           0.264             0.360Andorra         Europe   NaN            NaN            NaN  ...                   NaN                     NaN             NaN               NaN...                ...   ...            ...            ...  ...                   ...                     ...             ...               ...Western.Sahara  Africa  3.98            NaN            NaN  ...                   NaN                     NaN             NaN               NaNYemen             Asia  7.60            7.0            NaN  ...                  80.6                     1.9          32.406            69.552Yugoslavia      Europe  1.80            NaN            NaN  ...                   NaN                     NaN           1.782             9.072Zambia          Africa  5.49           25.0            7.9  ...                   NaN                     NaN          14.400            28.700Zimbabwe        Africa  4.68           48.0            NaN  ...                  77.7                    46.7           9.600            20.100[207 rows x 13 columns]

原著是一个有趣的人,若有侵权,请通知删除