pandas read_csv读取csv失败

Question

当我试图阅读印度尼西亚出生时的预期寿命时(https://data.worldbank.org/indicator/SP.DYN.LE00.IN?locations=ID如果你想查看，这是链接)我不能，这是我的代码

import pandas as pd
import matplotlib.pyplot as plt
lifeexpectacion = pd.read_csv("API_SP.DYN.LE00.IN_DS2_en_csv_v2_4770434.csv")
print(lifeexpectacion)

，错误是

File "D:programaizardata economymain.py", line 4, in <module>
lifeexpectacion = pd.read_csv("API_SP.DYN.LE00.IN_DS2_en_csv_v2_4770434.csv")

Answer 1

CSV的前4行包含标题、最后更新日期等信息。您需要跳过数据文件的前4行。使用pd.read_csv("API_SP.DYN.LE00.IN_DS2_en_csv_v2_4770434.csv", skiprows=4)

Answer 2

我下载了链接的文件，看看是否可以重新创建错误。这篇文章也有类似的问题。

csv的前四行是:

"Data Source","World Development Indicators",
"Last Updated Date","2022-12-22",

如果您删除这些行，它将按预期工作。是元数据让熊猫觉得应该只有两列，而实际上有67列。

Answer 3

works for me

df = pd.read_csv(r'D:tempAPI_SP.DYN.LE00.IN_DS2_en_csv_v2_4770434.csv',skiprows=4)
df
Out[149]: 
Country Name Country Code  ... 2021 Unnamed: 66
0                          Aruba          ABW  ...  NaN         NaN
1    Africa Eastern and Southern          AFE  ...  NaN         NaN
2                    Afghanistan          AFG  ...  NaN         NaN
3     Africa Western and Central          AFW  ...  NaN         NaN
4                         Angola          AGO  ...  NaN         NaN
..                           ...          ...  ...  ...         ...
261                       Kosovo          XKX  ...  NaN         NaN
262                  Yemen, Rep.          YEM  ...  NaN         NaN
263                 South Africa          ZAF  ...  NaN         NaN
264                       Zambia          ZMB  ...  NaN         NaN
265                     Zimbabwe          ZWE  ...  NaN         NaN
[266 rows x 67 columns]
df.columns
Out[150]: 
Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',
'1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',
'1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',
'1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',
'1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',
'1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',
'2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',
'2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021',
'Unnamed: 66'],
dtype='object')