Raised for a dtype incompatibility. This can happen whenever read_csv
or :None:None:`read_table` encounter non-uniform dtypes in a column(s) of a given CSV file.
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.
Warning raised when reading different dtypes in a column from a file.
read_csv
Read CSV (comma-separated) file into a DataFrame.
read_table
Read general delimited file into a DataFrame.
This example creates and reads a large CSV file with a column that contains :None:None:`int` and :None:None:`str`.
>>> df = pd.DataFrame({'a': (['1'] * 100000 + ['X'] * 100000 +
... ['1'] * 100000),
... 'b': ['b'] * 300000}) # doctest: +SKIP
... df.to_csv('test.csv', index=False) # doctest: +SKIP
... df2 = pd.read_csv('test.csv') # doctest: +SKIP
... # DtypeWarning: Columns (0) have mixed types
Important to notice that df2
will contain both :None:None:`str` and :None:None:`int` for the same input, '1'.
>>> df2.iloc[262140, 0] # doctest: +SKIP '1'This example is valid syntax, but we were not able to check execution
>>> type(df2.iloc[262140, 0]) # doctest: +SKIP <class 'str'>This example is valid syntax, but we were not able to check execution
>>> df2.iloc[262150, 0] # doctest: +SKIP 1This example is valid syntax, but we were not able to check execution
>>> type(df2.iloc[262150, 0]) # doctest: +SKIP <class 'int'>
One way to solve this issue is using the dtype
parameter in the read_csv
and :None:None:`read_table` functions to explicit the conversion:
>>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str}) # doctest: +SKIP
No warning was issued.
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