CODEWITHSUNDEEP
pythonpandasdataframe
Paw in Data
Paw in Data

Reputation: 1554

How can I read in row names as they were originally, using pandas.read_csv( )?

I need to read in a .csv file which contains a distance matrix, so it has identical row names and column names, and it's important to have them both. However, the code below can only get me a dataframe where row names are included in an extra "Unnamed: 0" column and the index become integers again, which is very inconvenient for the indexing later.

DATA = pd.read_csv("https://raw.githubusercontent.com/PawinData/UC/master/DistanceMatrix_shortestnetworks.csv")

I did check the documentation of pandas.read_csv and played with index_col, header, names, e.t.c but none seemed to work. Can anybody help me out here?

Upvotes: 1

Views: 4075

Answers (2)

Darsh Shukla
Darsh Shukla

Reputation: 389

This issue most likely exhibits because your CSV was saved along with its RangeIndex, which usually doesn't have a name. The fix would actually need to be done when saving the DataFrame data.to_csv('file.csv', index = False)

To read the unnamed column as the index. Specify an index_col=0 argument to pd.read_csv, this reads in the first column as the index.

data = pd.read_csv("https://raw.githubusercontent.com/PawinData/UC/master/DistanceMatrix_shortestnetworks.csv",index_col = 0)

And to drop the unnamed column use data.drop(data.filter(regex="Unname"),axis=1, inplace=True)

Upvotes: 1

jezrael
jezrael

Reputation: 862671

Use index_col=0 parameter for first column to index:

url = "https://raw.githubusercontent.com/PawinData/UC/master/DistanceMatrix_shortestnetworks.csv"
DATA = pd.read_csv(url, index_col=0)

print (DATA.head())
             Imperial  Kern  Los Angeles  Orange  Riverside  San Bernardino  \
Imperial            0     3            3       2          1               2   
Kern                3     0            1       2          2               1   
Los Angeles         3     1            0       1          2               1   
Orange              2     2            1       0          1               1   
Riverside           1     2            2       1          0               1   

             San Diego  San Luis Obispo  Santa Barbara  Ventura  
Imperial             1                4              4        4  
Kern                 3                1              1        1  
Los Angeles          2                2              2        1  
Orange               1                3              3        2  
Riverside            1                3              3        3

Upvotes: 1

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