Prevent pandas from reading "NA" as NaN

Question

The .csv-file I'm reading from contains cells with the value "NA". Pandas automatically converts these into NaN, which I don't want. I'm aware of the keep_default_na=False parameter, but that changes the dtype of the columns to object which means pd.get_dummies doesn't work correctly.

Is there any way to prevent pandas from reading "NA" as NaN without changing the dtype?

Answer 1

keep_default_na=False works for me

from io import StringIO
import pandas as pd

txt = """col1,col2
a,b
NA,US"""

print(pd.read_csv(StringIO(txt), keep_default_na=False))

  col1 col2
0    a    b
1   NA   US

---

Without it

print(pd.read_csv(StringIO(txt)))

  col1 col2
0    a    b
1  NaN   US

Answer 2

This is what Pandas documentation gives:

na_values : scalar, str, list-like, or dict, optional
Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’.

keep_default_na : bool, default True
Whether or not to include the default NaN values when parsing the data. Depending on whether na_values is passed in, the behavior is as follows:

If keep_default_na is True, and na_values are specified, na_values is appended to the default NaN values used for parsing.
If keep_default_na is True, and na_values are not specified, only the default NaN values are used for parsing.
If keep_default_na is False, and na_values are specified, only the NaN values specified na_values are used for parsing.
If keep_default_na is False, and na_values are not specified, no strings will be parsed as NaN.
Note that if na_filter is passed in as False, the keep_default_na and na_values parameters will be ignored.