exception pandas.errors.DtypeWarning[source]#

Warning raised when reading different dtypes in a column from a file.

Raised for a dtype incompatibility. This can happen whenever read_csv or read_table encounter non-uniform dtypes in a column(s) of a given CSV file.

See also


Read CSV (comma-separated) file into a DataFrame.


Read general delimited file into a DataFrame.


This warning is issued when dealing with larger files because the dtype checking happens per chunk read.

Despite the warning, the CSV file is read with mixed types in a single column which will be an object type. See the examples below to better understand this issue.


This example creates and reads a large CSV file with a column that contains int and str.

>>> df = pd.DataFrame(
...     {
...         "a": (["1"] * 100000 + ["X"] * 100000 + ["1"] * 100000),
...         "b": ["b"] * 300000,
...     }
... )  
>>> df.to_csv("test.csv", index=False)  
>>> df2 = pd.read_csv("test.csv")  
... # DtypeWarning: Columns (0) have mixed types

Important to notice that df2 will contain both str and int for the same input, ‘1’.

>>> df2.iloc[262140, 0]  
>>> type(df2.iloc[262140, 0])  
<class 'str'>
>>> df2.iloc[262150, 0]  
>>> type(df2.iloc[262150, 0])  
<class 'int'>

One way to solve this issue is using the dtype parameter in the read_csv and read_table functions to explicit the conversion:

>>> df2 = pd.read_csv("test.csv", sep=",", dtype={"a": str})  

No warning was issued.