There are several ways to get the number of elements present in a pandas Series object. And the class pandas series constructor provides you several attributes and methods to determine the features of the Series object.In the following example, we will learn about the size and shape attributes of the pandas Series object. The size attribute will return an integer value representing the count of the total elements present in a Series object, which works similarly to the python length function.The shape attribute will return a tuple with two elements in it, those two elements are integer values representing the ... Read More

The pandas series constructor will automatically create series index labels based on the given data. If you want to specify those index labels, we can give those index values separately by using the index keyword argument of the pandas series function.The Python dictionary is a data to the pandas series and we have not specified any index labels then, the keys of python dictionary values are taken as index labels.It is possible to specify or change the index labels of a pandas Series object after creation also. It can be done by using the index attribute of the pandas series ... Read More

Pandas series is 1-Dimensional ndarray with labeled data, which means every value present in a series is having a label representation which is nothing but each data have thor on index values.The index can be label names (object data) or it can be values. By default, it will assign an index value from 0 - n-1 (n is the length of series values). And it has the ability to define index values.Pandas Series function has an index keyword for specifying index values, and it takes input as an array with any kind of data in it. The data can be ... Read More

One of the common data for pandas is date-time, pandas have a different set of functionalities to perform any task related to work on date-time data.Pandas have date_range functions for generating a sequence of dates in a particular order, at the same time it has many other functions to work with these date-time data.We can create a pandas series object by using date-time data, let’s see an example for creating a pandas Series using date-time values.Exampleimport pandas as pd # creating range sequence of dates dates = pd.date_range('2021-06-14', periods=5, freq='D') #creating pandas Series with date index s = ... Read More

If you try to create a pandas Series object by using a python dictionary, the indices and values order of the series will depend on the order of key-value pairs in the dictionary.In order to set the specific index order in the series object, we can use the index attribute of the pandas Series method at the time of series creation.Let’s take an example and create a pandas Series with specific index order by using a python dictionary. To do this first we need to create a dictionary.Exampleimport pandas as pd # Creating dictionary dictionary = {'a': 64, 'b': ... Read More

We can create a pandas Series object by using a python dictionary by sending the dictionary data to the pandas Series method i.e. pandas.Series(). This pandas Series method will create a new Series object with the keys and value pairs from the python dictionary.All the keys in the dictionary will become the indices of the Series object, whereas all the values from the key-value pairs in the dictionary will become the values (data) of the Series object.Let’s see an example to create a pandas Series with a python dictionary, to do this we need to create a python dictionary first.Exampleimport ... Read More

A pandas Series is very similar to a 1-dimensional NumPy array, and we can create a pandas Series by using a NumPy array. To do this we need to import the NumPy module, as it is a prerequisite for the pandas package no need to install it separately.It is automatically installed by our package installer. So we can directly import the NumPy module into our workspace.If you don’t like to use this available version of NumPy, which is installed by the package installer we can install our required version of the NumPy package, as it is also an open-source package ... Read More

The pandas Series is a one-Dimensional data structure, it is a similar kind of one-Dimensional ndarray, and is capable of holding homogeneous elements with any data type. It can store integers, strings, floating-point numbers, Python objects, etc.Each value present in this pandas Series is represented with labels (indexes). By using these label names we can able to access any elements from the pandas Series.The default index value of the pandas Series is from 0 to the length of the series minus 1, or we can manually set the labels.Exampleimport pandas as pd S1 = pd.Series([11, 20, 32, 49, 65]) print(S1)ExplanationIn ... Read More

The data structure is a way of collecting data, organizing, and storing format that enables us to access and modify data in an efficient way. It is a collection of data types. It gives you the best way of organizing the items(values) in terms of memory.The python pandas package handles data in an effective way because it has two powerful data structures named Series and DataFrames.Series is nothing but a one-dimensional labeled array, which can be capable of holding any data type. it can store integer values, strings, floating-point numbers, etc. Each and every value in a Series is assigned ... Read More

To check whether the pandas package is installed or not in python we can simply verify the version. To get the version details of pandas we have two options.The first one is using the __version__ attribute.Exampleimport pandas as pd print(pd.__version__)ExplanationTo verify the version number of pandas, we can use this __version__ built-in attribute provided by pandas, this will return you the number specifying which version of pandas we have.Output1.1.5The number 1.1.5 represents the version of pandas that is already available.The second way is using the pandas show_versions() method.Exampleprint(pd.show_versions())show_versions() is a pandas method that will not only give you the information ... Read More