Prevent pandas from interpreting 'NA' as NaN in a string

Setting keep_default_na parameter does the trick.

Here is an example of keeping NA as string value while reading CSV file using Pandas.

data.csv:

country_name,country_code
Mexico,MX
Namibia,NA

read_data.py:

import pandas as pd
data = pd.read_csv("data.csv", keep_default_na=False)
print(data.describe())
print(data)

Output:

       country_name country_code
count             2            2
unique            2            2
top         Namibia           MX
freq              1            1

  country_name country_code
0       Mexico           MX
1      Namibia           NA

Reference:

• Pandas documentation to read CSV file

You could use parameters keep_default_na and na_values to set all NA values by hand docs:

import pandas as pd
from io import StringIO

data = """
PDB CHAIN SP_PRIMARY RES_BEG RES_END PDB_BEG PDB_END SP_BEG SP_END
5d8b N P60490 1 146 1 146 1 146
5d8b NA P80377 _ 126 1 126 1 126
5d8b O P60491 1 118 1 118 1 118
"""

df = pd.read_csv(StringIO(data), sep=' ', keep_default_na=False, na_values=['_'])

In [130]: df
Out[130]:
    PDB CHAIN SP_PRIMARY  RES_BEG  RES_END  PDB_BEG  PDB_END  SP_BEG  SP_END
0  5d8b     N     P60490        1      146        1      146       1     146
1  5d8b    NA     P80377      NaN      126        1      126       1     126
2  5d8b     O     P60491        1      118        1      118       1     118

In [144]: df.CHAIN.apply(type)
Out[144]:
0    <class 'str'>
1    <class 'str'>
2    <class 'str'>
Name: CHAIN, dtype: object

EDIT

All default NA values from na-values (as of pandas 1.0.0):

The default NaN recognized values are ['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A N/A', '#N/A', 'N/A', 'n/a', 'NA', '', '#NA', 'NULL', 'null', 'NaN', '-NaN', 'nan', '-nan', ''].

For me solution came from using parameter na_filter = False

df = pd.read_csv(file_, header=0, dtype=object, na_filter = False)