pyspark.pandas.DataFrame.info

DataFrame.info(verbose=None, buf=None, max_cols=None, show_counts=None)

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtype and column dtypes, non-null values and memory usage.

Parameters

verbosebool, optional

Whether to print the full summary.

bufwritable buffer, defaults to sys.stdout

Where to send the output. By default the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output.

max_colsint, optional

When to switch from the verbose to the truncated output. If the DataFrame has more than max_cols columns, the truncated output is used.

show_countsbool, optional

Whether to show the non-null counts.

New in version 4.0.0.

Returns

None

This method prints a summary of a DataFrame and returns None.

See also

DataFrame.describe

Generate descriptive statistics of DataFrame columns.

Examples

>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = ps.DataFrame(
...     {"int_col": int_values, "text_col": text_values, "float_col": float_values},
...     columns=['int_col', 'text_col', 'float_col'])
>>> df
   int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)  
<class 'pyspark.pandas.frame.DataFrame'>
Index: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      object
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), object(1)

Prints a summary of columns count and its dtypes but not per column information:

>>> df.info(verbose=False)  
<class 'pyspark.pandas.frame.DataFrame'>
Index: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)

Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open('%s/info.txt' % path, "w",
...           encoding="utf-8") as f:
...     _ = f.write(s)
>>> with open('%s/info.txt' % path) as f:
...     f.readlines()  
["<class 'pyspark.pandas.frame.DataFrame'>\n",
'Index: 5 entries, 0 to 4\n',
'Data columns (total 3 columns):\n',
' #   Column     Non-Null Count  Dtype  \n',
'---  ------     --------------  -----  \n',
' 0   int_col    5 non-null      int64  \n',
' 1   text_col   5 non-null      object \n',
' 2   float_col  5 non-null      float64\n',
'dtypes: float64(1), int64(1), object(1)']