Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Python Data Analysis, Second Edition
  • Toc
  • feedback
Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

By : Idris
4 (4)
close
Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

4 (4)
By: Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (16 chapters)
close
13
A. Key Concepts
15
C. Online Resources

Accessing databases from Pandas


We can give Pandas a database connection, such as the one in the previous example, or an SQLAlchemy connection. We will cover the latter in the later sections of this chapter. We will load the statsmodels sunactivity data, just as we did in the previous chapter, Chapter 7, Signal Processing and Time Series:

  1. Create a list of tuples to form the Pandas DataFrame:

            rows = [tuple(x) for x in df.values] 
    

    Contrary to the previous example, create a table without specifying data types:

                con.execute("CREATE TABLE sunspots(year, sunactivity)") 
    
  2. The executemany() method executes multiple statements; in this case, we will be inserting records from a list of tuples. Insert all the rows into the table and show the row count as follows:

            con.executemany("INSERT INTO sunspots(year, sunactivity) VALUES 
            (?, ?)", rows) 
            c.execute("SELECT COUNT(*) FROM sunspots") 
            print(c.fetchone()) 
    

    The number of rows in the table is printed as follows...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete