Question

I am reading an excel file that has several numerical and categorical data. The columns name_string contains characters in a foreign language. When I try to see the content of the name_string column, I get the results I want, but the foreign characters (that are displayed correctly in the excel spreadsheet) are displayed with the wrong encoding. Here is what I have:

import pandas as pd
df = pd.read_excel('MC_simulation.xlsx', 'DataSet', encoding='utf-8')
name_string = df.name_string.unique()
name_string.sort()
name_string

Producing the following:

array([u'4th of July', u'911', u'Abab', u'Abass', u'Abcar', u'Abced',
       u'Ceded', u'Cedes', u'Cedfus', u'Ceding', u'Cedtim', u'Cedtol',
       u'Cedxer', u'Chevrolet Corvette', u'Chuck Norris',
       u'Cristina Fern\xe1ndez de Kirchner'], dtype=object)

In the last line, the correctly encoded name should be Cristina Fernández de Kirchner. Can anybody help me with this issue?

Was it helpful?

Solution

Actually, the data is being parsed correctly into unicode, not strs. The u prefix indicate that the objects are unicode. When a list, tuple, or NumPy array is printed, Python shows the repr of the items in the sequence. So instead of seeing the printed version of the unicode, you see the repr:

In [160]: repr(u'Cristina Fern\xe1ndez de Kirchner')
Out[160]: "u'Cristina Fern\\xe1ndez de Kirchner'"

In [156]: print(u'Cristina Fern\xe1ndez de Kirchner')
Cristina Fernández de Kirchner

The purpose of the repr is to provide an unambiguous string representation for each object. The printed verson of a unicode can be ambiguous because of invisible or unprintable characters.

If you print the DataFrame or Series, however, you'll get the printed version of the unicodes:

In [157]: df = pd.DataFrame({'foo':np.array([u'4th of July', u'911', u'Abab', u'Abass', u'Abcar', u'Abced',
       u'Ceded', u'Cedes', u'Cedfus', u'Ceding', u'Cedtim', u'Cedtol',
       u'Cedxer', u'Chevrolet Corvette', u'Chuck Norris',
       u'Cristina Fern\xe1ndez de Kirchner'], dtype=object)})
   .....:    .....:    .....: 
In [158]: df
Out[158]: 
                               foo
0                      4th of July
1                              911
2                             Abab
3                            Abass
4                            Abcar
5                            Abced
6                            Ceded
7                            Cedes
8                           Cedfus
9                           Ceding
10                          Cedtim
11                          Cedtol
12                          Cedxer
13              Chevrolet Corvette
14                    Chuck Norris
15  Cristina Fernández de Kirchner

[16 rows x 1 columns]
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