Set Data Type For Specific Column When Using Read_csv From Pandas

I have a large csv file (~10GB), with around 4000 columns. I know that most of data i will expect is int8, so i set: pandas.read_csv('file.dat', sep=',', engine='c', header=None,

Solution 1:

If you are certain of the number you could recreate the dictionary like this:

dtype = dict(zip(range(4000),['int8'for _ inrange(3999)] + ['int32']))

Considering that this works:

import pandas as pd
import numpy as np
​
data = '''\
1,2,3
4,5,6'''
​
fileobj = pd.compat.StringIO(data)
df = pd.read_csv(fileobj, dtype={0:'int8',1:'int8',2:'int32'}, header=None)
​
print(df.dtypes)

Returns:

0int81int82int32
dtype: object

From the docs:

dtype : Type name or dict of column -> type, default None

Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} Use str or object to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion.

Solution 2:

Since you have no header, the column names are the integer order in which they occur, i.e. the first column is df[0]. To programmatically set the last column to be int32, you can read the first line of the file to get the width of the dataframe, then construct a dictionary of the integer types you want to use with the number of the columns as the keys.

import numpy as np
import pandas as pd

withopen('file.dat') as fp:
    width = len(fp.readline().strip().split(','))
    dtypes = {i: np.int8 for i inrange(width)}
    # update the last column's dtype
    dtypes[width-1] = np.int32

    # reset the read position of the file pointer
    fp.seek(0)
    df = pd.read_csv(fp, sep=',', engine='c', header=None, 
                     na_filter=False, dtype=dtypes, low_memory=False)