I got a CSV File. I got a column Product. One of the products in it, called 'NA'. I want to get all 'NA' products. But if you read the file in python, the NA products get to be NaN value.
CSV looks like this:
| Contact | Product |
|---|---|
| 1 | NA |
| 2 | ZE |
| 3 | HE |
| 3 |
In python i get this
| Contact | Product |
|---|---|
| 1 | NaN |
| 2 | ZE |
| 3 | HE |
| 4 | NaN |
How can i change this.
The read_csv method in pandas has the parameters na_values and keep_default_na, detailed in the documentation, which determine which text values are converted to NaN. At its most basic level, you could do:
import pandas as pd
df = pd.read_csv(your_file, keep_default_na=False)
And your 'NA' strings would no longer be converted to NaN. However this might have unintended consequences for other areas of your data, which would require refinement through na_values.
From pandas.read_csv doc
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’.
So, NA will automatically be read as NaN, but there is additional parameter called keep_default_na, you can pass False to this parameter if you want to change that default behavior:
df = pd.read_csv(path, keep_default_na=False)
Contact Product
0 1 NA
1 2 ZE
2 3 HE
3 3
But you may need to specify the values which you want pandas to represent as NaN, passing a list of such values, or a dictionary where key is the column and value is the values to represent as NaN.
df = pd.read_csv(path, keep_default_na=False, na_values=[''])
Contact Product
0 1 NA
1 2 ZE
2 3 HE
3 3 NaN
