Using pandas, I have exported to a csv file a dataframe whose cells contain tuples of strings. The resulting file has the following structure:
index,colA
1,"('a','b')"
2,"('c','d')"
Now I want to read it back using read_csv. However whatever I try, pandas interprets the values as strings rather than tuples. For instance:
In []: import pandas as pd
df = pd.read_csv('test',index_col='index',dtype={'colA':tuple})
df.loc[1,'colA']
Out[]: "('a','b')"
Is there a way of telling pandas to do the right thing? Preferably without heavy post-processing of the dataframe: the actual table has 5000 rows and 2500 columns.
A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes. Write DataFrame to a comma-separated values (csv) file. Read a comma-separated values (csv) file into DataFrame.
The csv. reader method returns a reader object which iterates over lines in the given CSV file. The numbers. csv file contains numbers.
Storing tuples in a column isn't usually a good idea; a lot of the advantages of using Series and DataFrames are lost. That said, you could use converters
to post-process the string:
>>> df = pd.read_csv("sillytup.csv", converters={"colA": ast.literal_eval})
>>> df
index colA
0 1 (a, b)
1 2 (c, d)
[2 rows x 2 columns]
>>> df.colA.iloc[0]
('a', 'b')
>>> type(df.colA.iloc[0])
<type 'tuple'>
But I'd probably change things at source to avoid storing tuples in the first place.
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