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Data Analysis and Processing with Pandas

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Introduction

Introduction Fundamental Data Structures in pandas Creating a Series Object Create a Series From a Numpy ndarray and a Scalar Value Manipulating Series Objects Creating a DataFrame From Arrays and Lists Creating a DataFrame From a Series Creating a DataFrame From a File Quiz

Basic Operations on DataFrames

Indexing and Slicing

Challenge - Overall Description of the Data

Challenge Solution Review

Grouping Data __ Part 1

Grouping Data __ Part 2

Deletion in a DataFrame

Select Data From DataFrame

Merging in a DataFrame

Challenge - Group by a Single Column and Analyze

Challenge Solution Review

Sorting a DataFrame

Apply Function to a DataFrame

Iteration Data in DataFrame

Challenge - Find the Most Popular Item

Challenge Solution Review

Other topics

Deal with Missing Value Export Data to File Convert DataFrame to a List String Type Challenge - Drop Duplicated Rows on SN Challenge Solution Review Deal With datetime Type Conversion Rename Columns Drop Duplicate Row Quiz

Improve performance

Speed up File Loading Reduce Memory Usage Speed up Column Operations Optimized Data Filtering

Conclusion

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Description

Description

The DataFrame provides many functions that show description operations, such as mean, median, and sum, that help you get a feel for the data.

For these functions, the default operations are performed column-wise. If you want to do row-wise operations, you can pass the parameter axis=1 to those functions.

mean

Calculate the mean of column or row. In the code below, there is a function called describe() that gives you mean, count, minimum and maximum values, three quartiles, and the standard deviation. This is very useful when you are in exploratory data analysis.

Python 3.5

import pandas as pd
# create a matrix, the size is 10*3
d = {"a":range(1, 10), "b": range(11,20), "c": range(21,30)}
df = pd.DataFrame(d)
print("---------------------------")
# calculate the mean of each column
print("The mean of each column is {}".format(df.mean().values))
# calculate the mean of each row
print("The mean of each row is {}".format(df.mean(axis=1).values))
# give a summary of your data on column wise
print(df.describe())

Line 8 shows what happens when you call ...