Python Tutorials 7: Data Analysis and Visualization: Pandas and Matplotlib
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Make Data Analysis Fun: A Journey of Discovery with Python
Hello friends! We talked about basic data types, variables, conditionals, loops, functions, lists, dictionaries, file operations, object-oriented programming and an introduction to web development in Python. Now we will explore the powerful tools that Python offers us in the world of data science! Today, data is everywhere: social media, e-commerce sites, sensors, devices... Making sense of this data and extracting insights allows businesses to make better decisions, scientists to make new discoveries and society to get better services. Python is one of the most popular programming languages for data analysis and data visualization .
Pandas: Powerful Data Analysis Tool
Pandas is one of the most widely used libraries for data analysis in Python. Pandas provides useful tools for organizing, processing, analyzing, and manipulating data in the form of tables. The most basic data structure in Pandas is called a data frame (DataFrame). A data frame can be thought of as a table with rows and columns. With Pandas, it is possible to create data frames, read data, filter, sort, group, combine, collect, and do many other operations. ```python import pandas as pd # Creating a dataframe data = {'name': ['Ali' 'Veli' 'Ayşe'] 'age': [30 25 28] 'city': ['Ankara' 'İstanbul' 'İzmir']} df = pd.DataFrame(data) # Printing the dataframe print(df) # Filtering the age column elderly = df[df['age'] > 27] print(elderly) ``` In this example, we created a dataframe with the columns "name", "age" and "city". Then, we filtered the age column to get information about people whose age is older than 27.
Matplotlib: The Art of Visualizing Data
Matplotlib is one of the most widely used libraries for data visualization in Python. Matplotlib provides useful functions to create various types of graphs (line graphs, bar graphs, pie charts, scatter plots, etc.). Visualizing data with Matplotlib is very important for making data more understandable, identifying trends, and extracting insights. ```python import matplotlib.pyplot as plt # Creating the dataset x = [1 2 3 4 5] y = [2 4 6 8 10] # Drawing the line graph plt.plot(xy) plt.xlabel('X-Axis') plt.ylabel('Y-Axis') plt.title('Line Graph') plt.show() ``` In this example, we created a dataset consisting of x and y values and used this dataset to draw a line graph.
Telling Stories with Data Analysis: Deriving Insights
Data analytics is not just about processing data, it’s about extracting meaningful information from that data and telling stories. Tools like Pandas and Matplotlib help us visualize those stories and present them in a way that everyone can understand. Data analytics is used by businesses to optimize their marketing strategies, scientists to make new discoveries, governments to solve social problems, and many other areas.
