Adventures in Machine Learning

Mastering Pandas Data Frame: Creating and Modifying Data Frames

Dataframes are a fundamental aspect of data analysis and manipulation. They are used in various programming languages to store and manipulate data and have become an integral part of modern data science.

In this article, we will explore what a pandas dataframe is, its benefits, and how to create one using a variety of data sources. 1)to Pandas Data Frame:

Pandas dataframe is a two-dimensional tabular data structure that stores data in rows and columns.

It is a type of data object that is used to store and manipulate data in a pandas library, which is widely used in data analysis and data science. The pandas dataframe is similar to the SQL table, but it is more flexible and user-friendly.

Pandas data frame is a crucial component of data manipulation and analysis in the Python programming language. Creating a Pandas Data Frame From Different Data Sources:

Pandas data frame can be created from various data sources such as CSV file, Excel file, dictionary, list, and so on.

Here are some ways to create a pandas dataframe:

a) Creating a Pandas Data Frame from CSV File:

CSV file is a Comma Separated Value file, and it is a plain text file that stores data in tabular form. The Pandas library provides a method called read_csv(), which reads a CSV file and returns a Pandas dataframe.

b) Creating a Pandas Data Frame from Excel File:

Similar to the CSV file, the Pandas library provides an excel() method, which can read an Excel file and return a Pandas dataframe. c) Creating a Pandas Data Frame from List:

A list is a Python data structure that stores data in a linear manner.

A pandas dataframe can be created from a list using the pandas.DataFrame() method. d) Creating a Pandas Data Frame from Dictionary:

A dictionary is a Python data structure that stores data in key-value pairs.

A pandas dataframe can be created from a dictionary using the pandas.DataFrame.from_dict() method. e) Creating a Pandas Data Frame from a list of lists:

A list of lists is a Python data structure that stores data in a tabular form.

A Pandas dataframe can be created from a list of lists using the pandas.DataFrame() method. f) Creating a Pandas Data Frame from a list of dictionaries:

A list of dictionaries is a Python data structure that stores data in key-value pairs.

A Pandas dataframe can be created from a list of dictionaries using the pandas.DataFrame.from_dict() method. g) Creating a Pandas Data Frame from a dictionary of ndarray/lists:

A dictionary of ndarray/lists is a Python data structure that stores data in a tabular form.

A Pandas dataframe can be created from a dictionary of ndarray/lists using the pandas.DataFrame() method. 2) Installing and Importing Pandas:

Before creating the pandas dataframe, we need to have the pandas library installed in the system.

Here’s how you can install the pandas library using pip:

a) Open your terminal or command prompt. b) Type “pip install pandas” and press enter.

After successfully installing the library, we need to import it in our Python program using the following code:

import pandas as pd

Conclusion:

Pandas data frame is a crucial component of data manipulation and analysis in Python programming. It has several benefits, such as it provides a flexible and user-friendly interface to manipulate tabular data.

In this article, we have learned what a pandas dataframe is, how to create it using various data sources, and how to install and import the pandas library into our Python program. The Pandas library is widely used in data analysis, data science, machine learning, and other related fields.

So, it is important to understand its basic concepts and functionality. 3) Creating a Data Frame from a Dictionary of Lists:

In Python, a dictionary of lists is a data structure that is used to store a collection of data where each item in the dictionary is a list of data.

To create a Pandas data frame from a dictionary of lists, we can use the pd.DataFrame() method. Here are the steps to create a dictionary of lists and then convert it into a Pandas data frame:

1.

Create a dictionary of lists:

To create a dictionary of lists, we start by defining a dictionary and then assign a list to each key of the dictionary. Here is an example:

“`

# Creating a dictionary of lists

dict_of_lists = {‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’],

‘Age’: [28, 31, 25, 24],

‘City’: [‘New York’, ‘Paris’, ‘London’, ‘Berlin’]}

“`

2.

Create a Pandas data frame from the dictionary:

Once we have the dictionary of lists, we can convert it to a Pandas data frame using the pd.DataFrame() method. This method takes in the dictionary as its argument and returns a data frame object.

Here is an example:

“`

# Creating a Pandas data frame from the dictionary

df = pd.DataFrame(dict_of_lists)

print(df)

“`

Output:

“`

Name Age City

0 Alice 28 New York

1 Bob 31 Paris

2 Charlie 25 London

3 David 24 Berlin

“`

As we can see, the dictionary of lists has been converted into a Pandas data frame with each key becoming a column of the data frame. 4) Adding a New Column to an Existing Data Frame:

Sometimes we may want to add a new column to an existing Pandas data frame.

We can achieve this in several ways. a) Using DataFrame indexing to add a new column:

One way to add a new column to a Pandas data frame is to use DataFrame indexing to assign a new column of data to the data frame.

Here is an example:

“`

# Adding a new column to the existing data frame

df[‘Country’] = [‘USA’, ‘France’, ‘UK’, ‘Germany’]

print(df)

“`

Output:

“`

Name Age City Country

0 Alice 28 New York USA

1 Bob 31 Paris France

2 Charlie 25 London UK

3 David 24 Berlin Germany

“`

In the code above, we have added a new column called “Country” to the data frame by assigning a list of country names to the column using DataFrame indexing. b) Using the in-built assign() method to add a new column:

Pandas also provides an in-built method called assign() that we can use to add a new column to an existing data frame.

The assign() method creates a new data frame with the added column and returns the new data frame. Here is an example:

“`

# Adding a new column to the existing data frame using assign() method

df1 = df.assign(Population = [8.399, 2.148, 8.9, 3.769])

print(df1)

“`

Output:

“`

Name Age City Country Population

0 Alice 28 New York USA 8.399

1 Bob 31 Paris France 2.148

2 Charlie 25 London UK 8.900

3 David 24 Berlin Germany 3.769

“`

In the above code, we have added a new column called “Population” to the data frame using the assign() method. c) Using the insert() method to add a new column:

The insert() method is another way to add a new column to a data frame.

This method allows us to specify the position of the new column in the data frame. Here is an example:

“`

# Adding a new column to the existing data frame using insert() method

df.insert(3, ‘Language’, [‘English’, ‘French’, ‘English’, ‘German’])

print(df)

“`

Output:

“`

Name Age City Language Country

0 Alice 28 New York English USA

1 Bob 31 Paris French France

2 Charlie 25 London English UK

3 David 24 Berlin German Germany

“`

In the above code, we have added a new column called “Language” to the data frame using the insert() method. The “3” in the insert() method specifies the position where the new column should be added.

In conclusion, adding a new column to a Pandas data frame is a common data manipulation task. We have discussed three ways to add a new column to a data frame, namely using DataFrame indexing, using the assign() method, and using the insert() method.

Each of these methods offers a way to customize the position and name of the new column. In this article, we have explored two essential concepts related to Pandas data frame: creating a data frame from different data sources and adding a new column to an existing data frame.

We have covered various methods of creating a data frame, including from a CSV file, an Excel file, a dictionary of lists, and more. Additionally, we have discussed three ways to add a new column to an existing data frame: using DataFrame indexing, the assign() method, and the insert() method.

These skills are essential for data manipulation and analysis in Python, and understanding how to use and work with data frames is crucial for any data science project. By mastering these fundamental concepts, we can efficiently analyze and manipulate data to draw meaningful insights, improve decision-making, and drive innovation in our work.

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