Introduction to Pandas
Introduction to Pandas
Pandas is a popular data manipulation library for Python. It provides data structures for efficiently storing and manipulating large datasets. In this article, we will introduce the basic functionalities of Pandas with examples.
Installation
Pandas can be easily installed using pip package manager. Open your terminal or command prompt and type the following command:
pip install pandas
Importing Pandas
Before we can use Pandas, we need to import it. This can be done using the following command:
import pandas as pd
Data Structures
Pandas provides two main data structures for storing and manipulating data: Series and DataFrame.
Series
A Series is a one-dimensional array-like object that can hold any data type. It is similar to a column in a spreadsheet or a SQL table. Here’s an example of creating a Series:
import pandas as pd
s = pd.Series([1, 3, 5, np.nan, 6, 8])
print(s)
Output:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
DataFrame
A DataFrame is a two-dimensional table-like data structure, where each column can have a different data type. It can be thought of as a collection of Series. Here’s an example of creating a DataFrame:
import pandas as pd
import numpy as np
data = {'name': ['John', 'Mary', 'Peter', 'Jeff', 'Lisa'],
'age': [23, 19, 42, 31, 24],
'country': ['USA', 'Canada', 'Australia', 'USA', 'Canada']}
df = pd.DataFrame(data)
print(df)
Output:
name age country
0 John 23 USA
1 Mary 19 Canada
2 Peter 42 Australia
3 Jeff 31 USA
4 Lisa 24 Canada
Creating Pandas DataFrame: Explained with Examples
A DataFrame is a two-dimensional table-like data structure, where each column can have a different data type. It can be thought of as a collection of Series. Pandas provides several ways to create a DataFrame, depending on the data source and the desired output format. In this article, we will introduce some of the most common methods of creating a Pandas DataFrame with examples.
From a Dictionary
One of the most common ways to create a DataFrame in Pandas is from a dictionary. The keys of the dictionary represent the column names, and the values represent the data. Here’s an example:
import pandas as pd
data = {'name': ['John', 'Mary', 'Peter', 'Jeff', 'Lisa'],
'age': [23, 19, 42, 31, 24],
'country': ['USA', 'Canada', 'Australia', 'USA', 'Canada']}
df = pd.DataFrame(data)
print(df)
Output:
name age country
0 John 23 USA
1 Mary 19 Canada
2 Peter 42 Australia
3 Jeff 31 USA
4 Lisa 24 Canada
From a CSV File
Another common way to create a DataFrame is from a CSV (comma-separated values) file. Pandas provides the?read_csv()?function for this purpose. Here's an example:
import pandas as pd
df = pd.read_csv('data.csv')
print(df)
Output:
id name age
0 1 John 23
1 2 Mary 19
2 3 Jeff 31
From a List of Lists
A DataFrame can also be created from a list of lists. Each inner list represents a row of data, and the outer list contains all the rows. Here’s an example:
import pandas as pd
data = [['John', 23, 'USA'],
['Mary', 19, 'Canada'],
['Peter', 42, 'Australia'],
['Jeff', 31, 'USA'],
['Lisa', 24, 'Canada']]
df = pd.DataFrame(data, columns=['name', 'age', 'country'])
print(df)
Output:
name age country
0 John 23 USA
1 Mary 19 Canada
2 Peter 42 Australia
3 Jeff 31 USA
4 Lisa 24 Canada
From a List of Dictionaries
Finally, a DataFrame can be created from a list of dictionaries. Each dictionary represents a row of data, and the keys of the dictionaries represent the column names. Here’s an example:
import pandas as pd
data = [{'name': 'John', 'age': 23, 'country': 'USA'},
{'name': 'Mary', 'age': 19, 'country': 'Canada'},
{'name': 'Peter', 'age': 42, 'country': 'Australia'},
{'name': 'Jeff', 'age': 31, 'country': 'USA'},
{'name': 'Lisa', 'age': 24, 'country': 'Canada'}]
df = pd.DataFrame(data)
print(df)
Output:
领英推荐
name age country
0 John 23 USA
1 Mary 19 Canada
2 Peter 42 Australia
3 Jeff 31 USA
4 Lisa 24 Canada
In conclusion, Pandas provides several ways to create a DataFrame, depending on the data source and the desired output format. The methods described in this article are just a few examples of what Pandas can do. Its capabilities go far beyond what we have covered here. If you are working with data in Python, Pandas is a must-have library.
Reading and Writing Data
Pandas provides functions for reading and writing data in various formats such as CSV, Excel, SQL, and more. Here’s an example of reading a CSV file:
import pandas as pd
df = pd.read_csv('data.csv')
print(df)
Output:
id name age
0 1 John 23
1 2 Mary 19
2 3 Jeff 31
Here’s an example of writing a DataFrame to a CSV file:
import pandas as pd
data = {'name': ['John', 'Mary', 'Jeff'],
'age': [23, 19, 31]}
df = pd.DataFrame(data)
df.to_csv('data.csv', index=False)
Importing Data with Pandas in Python: A Comprehensive Guide with Examples
Pandas is a popular data manipulation library for Python. It provides data structures for efficiently storing and manipulating large datasets. One of the most important functionalities of Pandas is its ability to import data from various sources. In this article, we will introduce some of the most common ways of importing data with Pandas, along with examples.
Importing Data from CSV Files
CSV (comma-separated values) files are one of the most common ways of storing and sharing data. Pandas provides the?read_csv()?function for reading data from CSV files. Here's an example:
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head())
Output:
id name age
0 1 John 23
1 2 Mary 19
2 3 Jeff 31
By default,?read_csv()?assumes that the first row of the CSV file contains the column names. If your CSV file does not have a header row, you can specify the column names using the?names?parameter:
import pandas as pd
df = pd.read_csv('data.csv', names=['id', 'name', 'age'])
print(df.head())
Output:
id name age
0 1 John 23
1 2 Mary 19
2 3 Jeff 31
Importing Data from Excel Files
Excel files are another common way of storing and sharing data. Pandas provides the?read_excel()?function for reading data from Excel files. Here's an example:
import pandas as pd
df = pd.read_excel('data.xlsx')
print(df.head())
Output:
id name age
0 1 John 23
1 2 Mary 19
2 3 Jeff 31
By default,?read_excel()?reads the first sheet of the Excel file. If your Excel file has multiple sheets, you can specify the sheet name or index using the?sheet_name?parameter:
import pandas as pd
df = pd.read_excel('data.xlsx', sheet_name='Sheet2')
print(df.head())
Output:
id weight
0 1 70
1 2 65
2 3 80
Importing Data from SQL Databases
Pandas can also import data from SQL databases. Pandas provides the?read_sql()?function for this purpose. Here's an example:
import pandas as pd
import sqlite3
conn = sqlite3.connect('data.db')
df = pd.read_sql('SELECT * FROM users', conn)
print(df.head())
Output:
id name age
0 1 John 23
1 2 Mary 19
2 3 Jeff 31
In this example, we first connect to a SQLite database using the?sqlite3?module. Then, we use the?read_sql()?function to read data from the?users?table.
Importing Data from APIs
Pandas can also import data from APIs (application programming interfaces). APIs provide a way of accessing data from web services. Pandas provides the?read_json()?function for reading JSON (JavaScript Object Notation) data from APIs. Here's an example:
import pandas as pd
import requests
response = requests.get('<https://jsonplaceholder.typicode.com/users>')
data = response.json()
df = pd.read_json(data)
print(df.head())
Output:
id name username ... phone website company
0 1 Leanne Graham Bret ... 1-770-736-8031 x56442 hildegard.org Romaguera-Crona
1 2 Ervin Howell Antonette ... 010-692-6593 x09125 anastasia.net Deckow-Crist
2 3 Clementine Bauch Samantha ... 1-463-123-4447 x3321 ramiro.info Romaguera-Jacobson
3 4 Patricia Lebsack Karianne ... 493-170-9623 x156 kale.biz Robel-Corkery
4 5 Chelsey Dietrich Kamren ... (254)954-1289 x2544 demarco.info Keebler LLC
In this example, we use the?requests?module to make a GET request to the JSONPlaceholder API, which returns a list of users in JSON format. Then, we use the?read_json()?function to read the JSON data into a DataFrame.
In conclusion, Pandas is a powerful data manipulation library for Python that provides efficient data structures for storing and manipulating large datasets. It offers a variety of ways to create and import data into its two main data structures, Series and DataFrame. These data structures can be used to analyze and manipulate data in many different ways. Additionally, Pandas provides functions for reading and writing data in various formats, such as CSV, Excel, SQL, and more. With its numerous capabilities, Pandas is a must-have library for anyone working with data in Python.