Analyzing Data Activity with Pandas


Pandas is a very popular tool in the data science field. It is greatly used in analyzing data activity.

The process of cleansing, converting, and modeling data in order to find relevant information for corporate decision-making is known as data analysis. Extracting usable information from data and making decisions based on that analysis are the goals of data analysis.

In this article, we will learn about the role of pandas in data science.

Python or C back-end source code is available from the Pandas library.

Two strategies may be used to accomplish data analysis −

  • Series

  • DataFrames

Pandas Series

Pandas define an array called a Series that can be utilized to store any type of data. It is a singular column of a grid or a 1D array. A series is a collection of values that are individually associated with a distinct label and have unique index values linked to each row. When a series is created, these distinctive index values are dynamically defined.

Create new series

Creating an empty series −

import pandas as pd
s=pd.Series()

Let's look at other instances.

Case 1: Only Scalar values

import pandas as pd
array= [9,6,3,2,8,5]
seri= pd.Series(array)
print(seri)

Output

0 9
1 6
2 3
3 2
4 8
5 5
dtype: int64

Printing series with index in Roman Numeral −

index=['i' , 'ii', 'iii', 'iv', 'v', 'vi']
seri1= pd.Series(array, index)
print(seri1)

Output

i      9
ii     6
iii    3
iv     2
v      8
vi     5
dtype: int64

Case 2: Dictionary values

import pandas as pd
dict= {'i' : 1 , 'j': 2, 'k': 3, 'l': 4}
s= pd.Series(dict)
print(s)

Output

i  1
j  2
k  3
l  4
dtype: int64

Case 3: Multidimensional arrays

import pandas as pd
array= [[1,2], [3,4,5], [6,7,8]]
s=pd.Series(array)
print(s)

Output

0       [1, 2]
1    [3, 4, 5]
2    [6, 7, 8]
dtype: object

Pandas DataFrame

A 2D data structure made up of rows and columns are known as a Pandas DataFrame. The following crucial Pandas structure consists of a collection of series and is a multidimensional table on an Excel sheet. It simplifies tabular data, where each row represents an observation and each column a variable.

Here is an illustration that shows how the DataFrame functions. The same is seen in the code snippet below.

import pandas as pd
data= {
   "calories": [100,200,300],
   "duration" :[20,30,35]
}
df=pd.DataFrame(data)
print(df)

Output

calories duration
0   100      20
1   200      30
2   300      35

Let's look at other instances.

Case1: Scalar values

import pandas as pd
dic1= {'i' : 1 , 'j': 2, 'k': 3, 'l': 4}
dic2= {'i' :5 , 'j': 6, 'k': 7, 'l': 8, 'm' :9}
instance= {'first' : dic1, 'second': dic2}
df= pd.DataFrame(instance)
print(df)

Output

first  second
i    1.0       5
j    2.0       6
k    3.0       7
l    4.0       8
m    NaN       9

Case 2: Series data

import pandas as pd
s1=pd.Series([1,2,3,4,5])
s2=pd.Series(['a','b','c'])
s3=pd.Series(['A','B','C','D'])
instance= {'first' : s1, 'second': s2, 'third': s3}
df= pd.DataFrame(instance)
print(df)

Output

first second third
0      1      a     A
1      2      b     B
2      3      c     C
3      4    NaN     D
4      5    NaN   NaN

Case3 : 2D NumPy array

When building a DataFrame, a 2D array's dimensions must remain constant.

import pandas as pd
array1= [[1,2], [3,4,5], [6,7,8]]
array2= [['a','b'], ['c','d','e'], ['f','g','h']]
instance= {'first' :array1, 'second': array2}
df= pd.DataFrame(instance)
print(df)

Output

first     second
0     [1, 2]     [a, b]
1    [3, 4, 5]  [c, d, e]
2    [6, 7, 8]  [f, g, h]

Pandas in Data Science and Machine Learning

After being gathered, the data is kept in multiple databases from which it may be accessed for use in different data science activities and projects. An endeavor including data science has two stages −

  • Data cleaning stage

  • Analyzing exploratory data

These stages provide you with a top-notch dataset to interact with. Starting on this filtered dataset, a model for machine learning may be created. The Pandas library provides a broad range of capabilities that let you carry out operations from the time you first get raw data until the time you provide high-quality data for additional testing.

The learnings from the data analysis serve as a springboard for developers to choose the appropriate path for in-depth research and machine learning models.

Comparing several subsets that were created using various Pandas operations and processes can be part of statistical analysis

We've seen examples of data manipulation and data analysis with Pandas. Let's take a closer look at how data is processed for machine learning.

How Pandas speeds the creation of ML models

Every machine learning project requires a large investment of time. This is due to the fact that it uses several techniques, such as studying the fundamental trends and patterns before creating an ML model. The Python Pandas package provides a variety of tools for manipulating and analyzing data.

Pandas are essential for creating ML models. Here are several procedures.

Importing the data

A broad variety of tools are available in the Pandas library to read data from various sources. The CSV file may be used as a dataset function, which offers a wide range of choices for data processing. Here is the section of code that imports the data.

Locating missing data

Pandas provide a tool to determine how many ways there are to handle missing data. To begin with, you may examine the data and identify any missing values by using the ISNA() method. This function examines each row and column's value. It returns True if the value is absent and False otherwise.

Visualizing the data

The data may be effectively seen by plotting in Pandas. In a DataFrame, you may use the plt.plot() function. You must import Matplotlib before you can plot. Histograms, lines, boxplots, scatter plots, and bars are just a few of the data visualisation forms supported by this function. When used in conjunction with the data aggregation tool, the graphing function is quite helpful.

Transformation of features

Pandas provide a variety of feature transformation functions. Since the most widely used machine libraries only take numerical data, the non-numeric characteristic must be transformed. The function gets dummies, which is available in Pandas, transforms each distinct value into a binary column when it is applied to a data column.

Conclusion

Pandas is a popular data science and data analysis tool used by many professionals and data scientists. They can handle the data and create machine learning models thanks to Pandas DataFrame. Although there is a slight learning curve, it greatly improves the effectiveness of data manipulation.

Updated on: 09-Jan-2023

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