python – Pandas read_csv dtype read all columns but few as string

python – Pandas read_csv dtype read all columns but few as string

EDIT – sorry, I misread your question. Updated my answer.

You can read the entire csv as strings then convert your desired columns to other types afterwards like this:

df = pd.read_csv(/path/to/file.csv, dtype=str)
# example df; yours will be from pd.read_csv() above
df = pd.DataFrame({A: [1, 3, 5], B: [2, 4, 6], C: [x, y, z]})
types_dict = {A: int, B: float}
for col, col_type in types_dict.items():
    df[col] = df[col].astype(col_type)

Another approach, if you really want to specify the proper types for all columns when reading the file in and not change them after: read in just the column names (no rows), then use those to fill in which columns should be strings

col_names = pd.read_csv(file.csv, nrows=0).columns
types_dict = {A: int, B: float}
types_dict.update({col: str for col in col_names if col not in types_dict})
pd.read_csv(file.csv, dtype=types_dict)

I recently encountered the same issue, though I only have one csv file so I dont need to loop over files. I think this solution can be adapted into a loop as well.

Here I present a solution I used. Pandas read_csv has a parameter called converters which overrides dtype, so you may take advantage of this feature.

An example code is as follows:
Assume that our data.csv file contains all float64 columns except A and B which are string columns. You may read this file using:

df = pd.read_csv(data.csv, dtype = float64, converters = {A: str, B: str})  

The code gives warnings that converters override dtypes for these two columns A and B, and the result is as desired.

Regarding looping over several csv files all one needs to do is to figure out which columns will be exceptions to put in converters. This is easy if files have a similar pattern of column names, otherwise, it would get tedious.

python – Pandas read_csv dtype read all columns but few as string

You can do the following:

                    customer_id: int32,
                    product_id: int32,
                    subcategory_id: int16,
                    category_id: int16,
                    gender: int8,
                    views: int8,
                    purchased: int8,
                    added: int8,
                    time_on_page: float16,

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