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Data Analysis & Processing with Pandas

Description

Explore how to use Pandas functions to calculate statistics like mean, sum, and standard deviation on DataFrames. Understand how to perform operations on columns and rows, and learn techniques to analyze entire DataFrames efficiently for effective exploratory data analysis.

We'll cover the following...

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 ...