I am trying to read in a dataset called df1, but it does not work
import pandas as pd
df1=pd.read_csv("https://raw.githubusercontent.com/tuyenhavan/Statistics/Dataset/World_Life_Expectancy.csv",sep=";")
df1.head()
Here are huge errors from the above code, but this is the most relevant
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 18: invalid start byte
The 0x92 byte can be decoded using the cp1252 encoding. Encoding is the process of converting a string to a bytes object and decoding is the process of converting a bytes object to a string . When decoding a bytes object, we have to use the same encoding that was used to encode the string to a bytes object.
The UnicodeDecodeError normally happens when decoding an str string from a certain coding. Since codings map only a limited number of str strings to unicode characters, an illegal sequence of str characters will cause the coding-specific decode() to fail.
The data is indeed not encoded as UTF-8; everything is ASCII except for that single 0x92 byte:
b'Korea, Dem. People\x92s Rep.'
Decode it as Windows codepage 1252 instead, where 0x92 is a fancy quote, ’:
df1 = pd.read_csv("https://raw.githubusercontent.com/tuyenhavan/Statistics/Dataset/World_Life_Expectancy.csv",
                  sep=";", encoding='cp1252')
Demo:
>>> import pandas as pd
>>> df1 = pd.read_csv("https://raw.githubusercontent.com/tuyenhavan/Statistics/Dataset/World_Life_Expectancy.csv",
...                   sep=";", encoding='cp1252')
>>> df1.head()
                   2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  \
0     Afghanistan  55.1  55.5  55.9  56.2  56.6  57.0  57.4  57.8  58.2  58.6
1         Albania  74.3  74.7  75.2  75.5  75.8  76.1  76.3  76.5  76.7  76.8
2         Algeria  70.2  70.6  71.0  71.4  71.8  72.2  72.6  72.9  73.2  73.5
3  American Samoa    ..    ..    ..    ..    ..    ..    ..    ..    ..    ..
4         Andorra    ..    ..    ..    ..    ..    ..    ..    ..    ..    ..
   2010  2011  2012  2013  Unnamed: 15  2014  2015
0  59.0  59.3  59.7  60.0          NaN  60.4  60.7
1  77.0  77.2  77.4  77.6          NaN  77.8  78.0
2  73.8  74.1  74.3  74.6          NaN  74.8  75.0
3    ..    ..    ..    ..          NaN    ..    ..
4    ..    ..    ..    ..          NaN    ..    ..
I note however, that Pandas seems to take the HTTP headers at face value too and produces a Mojibake when you load your data from a URL. When I save the data directly to disk, then load it with pd.read_csv() the data is correctly decoded, but loading from the URL produces re-coded data:
>>> df1[' '][102]
'Korea, Dem. People’s Rep.'
>>> df1[' '][102].encode('cp1252').decode('utf8')
'Korea, Dem. People’s Rep.'
This is a known bug in Pandas. You can work around this by using urllib.request to load the URL and pass that to pd.read_csv() instead:
>>> import urllib.request
>>> with urllib.request.urlopen("https://raw.githubusercontent.com/tuyenhavan/Statistics/Dataset/World_Life_Expectancy.csv") as resp:
...     df1 = pd.read_csv(resp, sep=";", encoding='cp1252')
...
>>> df1[' '][102]
'Korea, Dem. People’s Rep.'
It turned out that the csv created in mac os is being parsed on a windows machine, I got the UnicodeDecodeError. To get rid of this error, try passing argument encoding='mac-roman' to read_csv method of pandas library.
import pandas as pd
df1=pd.read_csv("https://raw.githubusercontent.com/tuyenhavan/Statistics/Dataset/World_Life_Expectancy.csv",sep=";", encoding='mac_roman')
df1.head()
Output:
    2000    2001    2002    2003    2004    2005    2006    2007    2008    2009    2010    2011    2012    2013    Unnamed: 15 2014    2015
0   Afghanistan 55.1    55.5    55.9    56.2    56.6    57.0    57.4    57.8    58.2    58.6    59.0    59.3    59.7    60.0    NaN 60.4    60.7
1   Albania 74.3    74.7    75.2    75.5    75.8    76.1    76.3    76.5    76.7    76.8    77.0    77.2    77.4    77.6    NaN 77.8    78.0
2   Algeria 70.2    70.6    71.0    71.4    71.8    72.2    72.6    72.9    73.2    73.5    73.8    74.1    74.3    74.6    NaN 74.8    75.0
3   American Samoa  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  NaN ..  ..
4   Andorra ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  NaN ..  ..
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