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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Concatenating and appending DataFrames


The pandas DataFrame allows operations that are similar to the inner and outer joins of database tables. We can append and concatenate rows as well. To practice appending and concatenating of rows, we will reuse the DataFrame from the previous section. Let's select the first three rows:

print "df :3\n", df[:3]

Check that these are indeed the first three rows:

df :3
       Food  Number     Price Weather
0      soup       8  3.745401    cold
1      soup       5  9.507143     hot
2  icecream       4  7.319939    cold

The concat() function concatenates DataFrames. For example, we can concatenate a DataFrame that consists of three rows to the rest of the rows, in order to recreate the original DataFrame:

print "Concat Back together\n", pd.concat([df[:3], df[3:]])

The concatenation output appears as follows:

Concat Back together
        Food  Number     Price Weather
0       soup       8  3.745401    cold
1       soup       5  9.507143     hot
2   icecream  ...
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