Book Image

Hands-On Data Analysis with Pandas

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas

By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Table of Contents (21 chapters)
Free Chapter
1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications - Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Cleaning up the data

Let's move on to the 3-cleaning_data.ipynb notebook for our discussion on data cleaning. We will begin by importing pandas and reading in the data/nyc_temperatures.csv file, which contains the maximum daily temperature (TMAX), minimum daily temperature (TMIN), and the average daily temperature (TAVG) from the LaGuardia airport station in New York City for October 2018:

>>> import pandas as pd

>>> df = pd.read_csv('data/nyc_temperatures.csv')
>>> df.head()

The data we retrieved from the API is in the long format; for our analysis, we want it in the wide format, but we will address that in the Pivoting DataFrames section later this chapter:

attributes datatype date station value
0 H,,S, TAVG 2018-10-01T00:00:00 GHCND:USW00014732 21.2
1 ,,W,2400 TMAX 2018-10-01T00:00:00 GHCND:USW00014732 25.6
2 ,,W,2400 TMIN...