Amazingly, it also takes a function! example: In [1]: s = pd.Series ( ["A","B","C"], name="foo") In [2]: s Out [2]: 0 A 1 B 2 C Name: foo, dtype: object In [3]: pd.DataFrame (s) Out [4]: foo 0 A 1 B 2 C. If you don't … The keys, levels, and names arguments are all optional. Pandas Change Column Names Method 1 – Pandas Rename. import numpy as np import pandas as pd. df.squeeze() In this guide, you’ll see 3 scenarios of converting: Single DataFrame column into a Series … The first method that we suggest is using Pandas Rename. index, columns scalar, list-like, dict-like or function, optional. The labels need not be unique but must be a hashable type. Pandas Series.dt.day_name () function return the day names of the DateTimeIndex with specified locale. pandas.Series.name¶ Series.name¶ Return name of the Series. Returns : Index of day names. Syntax: Series.dt.day_name (*args, **kwargs) Parameter : locale : Locale determining the language in which to return the day name. Pandas series is a One-dimensional ndarray with axis labels. Syntax: Series.rename(index=None, **kwargs) Parameter : index : dict-like or functions are transformations to apply to the index copy : Also copy underlying data inplace : Whether to return a new Series. Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). pandas.Series. Syntax: Series.dt.month_name (*args, **kwargs) Pandas Series.dt.month_name () function return the month names of the DateTimeIndex with specified locale. Combine two Series. Default is English locale. Rename takes a dict with a key of your old column name and a key of your new column name. Enter search terms or a module, class or function name. Pandas Series.rename() function is used to alter Series index labels or name for the given Series object. Pandas: Convert a dataframe column into a list using Series.to_list() or numpy.ndarray.tolist() in python Pandas : Loop or Iterate over all or certain columns of a dataframe Python Pandas : Select Rows in DataFrame by conditions on multiple columns If True then value of copy is ignored. pandas.Series.rename_axis¶ Series.rename_axis (mapper = None, index = None, columns = None, axis = None, copy = True, inplace = False) [source] ¶ Set the name of the axis for the index or columns. You can convert Pandas DataFrame to Series using squeeze:. Out [2]: p 1.0 q 2.0 r 2.0 s NaN dtype: float64. Out [3]: p 2.0 q NaN s 1.0 t 1.0 dtype: float64. Navigation. Examples. Pandas series with two different lists # series with two different lists import pandas as pd names = ['rakesh', 'vishank', 'nikunj', 'unnati', 'vipul'] city = ['GZB', 'Delhi', 'Meerut', 'Pune', 'Panji'] s = pd.Series(names, index=city) print(s) output Parameters mapper scalar, list-like, optional. A walkthrough of how this method fits in with other tools for combining pandas objects can be found here. In [2]: x = pd.Series( [1, 2, 2, np.nan], index=['p', 'q', 'r', 's']) x. >>> s1 = pd.Series( ['a', 'b']) >>> s2 = pd.Series( ['c', 'd']) >>> pd.concat( [s1, s2]) 0 a 1 b 0 c 1 d dtype: object. So that when you'll put that in a DataFrame, the column will be named according to the name parameter. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. In [3]: y = pd.Series( [2, np.nan, 1, 1], index=['p', 'q', 's', 't']) y. Value to set the axis name attribute. A pandas Series can be created using the following constructor − pandas.Series( data, index, dtype, copy) The parameters of the constructor are as follows − The axis labels are collectively called index. Other tools for combining pandas objects can be found here with other tools combining. 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