Python | Pandas Series.to_string()
Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.to_string()
function render a string representation of the Series.
Syntax: Series.to_string(buf=None, na_rep=’NaN’, float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None)
Parameter :
buf : buffer to write to
na_rep : string representation of NAN to use, default ‘NaN’
float_format : formatter function to apply to columns’ elements if they are floats default None
header : Add the Series header (index name)
index : Add index (row) labels, default True
length : Add the Series length
dtype : Add the Series dtype
name : Add the Series name if not None
max_rows : Maximum number of rows to show before truncating.
Returns : Formatted string.
Example #1: Use Series.to_string()
function to render a string representation of the given series object.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' , 'Moscow' ])
didx = pd.DatetimeIndex(start = '2014-08-01 10:00' , freq = 'W' ,
periods = 6 , tz = 'Europe/Berlin' )
sr.index = didx
print (sr)
|
Output :
Now we will use Series.to_string()
function to render string representation to this series object.
Output :
As we can see in the output, the Series.to_string()
function has successfully rendered a string representation to the given object.
Example #2: Use Series.to_string()
function to render a string representation of the given series object.
import pandas as pd
sr = pd.Series([ 19.5 , 16.8 , 22.78 , 20.124 , 18.1002 ])
print (sr)
|
Output :
Now we will use Series.to_string()
function to render string representation to this series object.
Output :
As we can see in the output, the Series.to_string()
function has successfully rendered a string representation to the given object.
Last Updated :
05 Feb, 2019
Like Article
Save Article
Share your thoughts in the comments
Please Login to comment...