pyspark.pandas.Series.to_string¶
-
Series.to_string(buf: Optional[IO[str]] = None, na_rep: str = 'NaN', float_format: Optional[Callable[[float], str]] = None, header: bool = True, index: bool = True, length: bool = False, dtype: bool = False, name: bool = False, max_rows: Optional[int] = None) → Optional[str]¶ Render a string representation of the Series.
Note
This method should only be used if the resulting pandas object is expected to be small, as all the data is loaded into the driver’s memory. If the input is large, set max_rows parameter.
- Parameters
- bufStringIO-like, optional
buffer to write to
- na_repstring, optional
string representation of NAN to use, default ‘NaN’
- float_formatone-parameter function, optional
formatter function to apply to columns’ elements if they are floats default None
- headerboolean, default True
Add the Series header (index name)
- indexbool, optional
Add index (row) labels, default True
- lengthboolean, default False
Add the Series length
- dtypeboolean, default False
Add the Series dtype
- nameboolean, default False
Add the Series name if not None
- max_rowsint, optional
Maximum number of rows to show before truncating. If None, show all.
- Returns
- formattedstring (if not buffer passed)
Examples
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)], columns=['dogs', 'cats']) >>> print(df['dogs'].to_string()) 0 0.2 1 0.0 2 0.6 3 0.2
>>> print(df['dogs'].to_string(max_rows=2)) 0 0.2 1 0.0