Introduction to Python Libraries : Python libraries contain a collection of builtin modules that allow us to perform many actions without writing detailed programs for it. Each library in Python contains a large number of modules that one can import and use. NumPy, Pandas and Matplotlib are three well-established Python libraries for scientific and analytical use. These libraries allow us to manipulate, transform and visualise data easily and efficiently. NumPy, which stands for ‘Numerical Python’, is a library we discussed in class XI. Recall that, it is a package that can be used for numerical data analysis and scientific computing. NumPy uses a multidimensional array object and has functions and tools for working with these arrays. Elements of an array stay together in memory, hence, they can be quickly accessed. In this section we will about how to create series using pandas.
In this chapter we will learn about the important library PANDAS. PANDAS (PANel DAta) is a high-level data manipulation tool used for analysing data. It is very easy to import and export data using Pandas library which has a very rich set of functions. It is built on packages like NumPy and Matplotlib and gives us a single, convenient place to do most of our data analysis and visualisation work. Pandas has three important data structures, namely – Series, DataFrame and Panel to make the process of analysing data organised, effective and efficient.
The Matplotlib library in Python is used for plotting graphs and visualisation. In this chapter we will learn about only Pandas Matplotlib library and Numpy we will discuss in upcoming articles. In this article we will just focus on PANDAS library.
Installing Pandas Installing Pandas is very similar to installing NumPy. To install Pandas from command line, we need to type in:
pip install pandas
Note that both NumPy and Pandas can be installed only when Python is already installed on that system. The same is true for other libraries of Python. Steps to install pandas in You Python. There are several methods to install PANDAS in python. we will discuss some of them to install pandas in python. Click here to Know how to install pandas in Pyhton.
Data Structure in Pandas
A data structure is a collection of data values and operations that can be applied to that data. It enables efficient storage, retrieval and modification to the data. For example, we have already worked with a data structure ndarray in NumPy in Class XI. Recall the ease with which we can store, access and update data using a NumPy array. Two commonly used data structures in Pandas that we will cover in this book are:
- Series
- DataFrame
>>> import pandas as pd #import Pandas with alias pd>>> series1 = pd.Series([10,20,30]) #create a Series>>> print(series1) #Display the series
Index Value
Observe that output is shown in two columns - the index is on the left and the data value is on the right. by default indices range from 0 through N – 1 for N number of data.
The following example has a numeric index in random order.
>>> series2 = pd.Series(["Kavi","Shyam","Ra vi"], index=[3,5,1])
>>> print(series2) #Display the series
Output:
3 Kavi
5 Shyam
1 Ravi
dtype: object
We can also use letters or strings as indices, for example:
>>> series2 = pd.Series([2,3,4],index=["Feb","M ar","Apr"])
>>> print(series2) #Display the series
Output:
Feb 2
Mar 3
Apr 4
dtype: int64
Creation of Series from NumPy Arrays
We can create a series from a one-dimensional (1D) NumPy array, as shown below: Activity 2.1 Create a series having names of any five famous monuments of India and assign their States as index values.
>>> import numpy as np # import NumPy with alias np
>>> import pandas as pd
>>> array1 = np.array([1,2,3,4])
>>> series3 = pd.Series(array1)
>>> print(series3)
Output:
0 1
1 2
2 3
3 4
dtype: int32
We can also define Index as follows:
>>> import numpy as np # import NumPy with alias np
>>> import pandas as pd
>>> array1 = np.array([1,2,3,4])
>>> series4 = pd.Series(array1, index = ["Jan", "Feb", "Mar", "Apr"])
>>> print(series4)
Out Put of the above code:
Jan 1
Feb 2
Mar 3
Apr 4
dtype: int32
Creation of Series from Dictionary
Recall that Python dictionary has key: value pairs and a value can be quickly retrieved when its key is known. Dictionary keys can be used to construct an index for a Series, as shown in the following example. Here, keys of the dictionary dict1 become indices in the series.
>>> dict1 = {'India': 'NewDelhi', 'UK': 'London', 'Japan': 'Tokyo'}
>>> print(dict1) #Display the dictionary {'India': 'NewDelhi', 'UK': 'London', 'Japan': 'Tokyo'}
>>> series8 = pd.Series(dict1)
>>> print(series8) #Display the series
Out Put of the above code:
India New Delhi
UK London
Japan Tokyo
dtype: object
In this article we learn how we can define series in python with pandas. We will continue this chapter, in next article we will discuss about How we can access this series and different method.
Click here to go Next Article.
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