Introduction to Data Science with Python/

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Useful Methods and Operations

Useful Methods and Operations

Let’s learn about some useful methods and selecting data from DataFrames using pandas.

We'll cover the following...

Useful methods

There are lots of options to explore that are available in pandas, including describe(), which gives the basic statistics on our data. We have already covered some of them, like head(), isnull(), dropna(), fillna(), and others.

In this lesson, we’ll explore some more general-purpose operations and revise what we’ve learned in the previous lessons.

Let’s create a DataFrame to get hands-on experience with these operations. We’ll repeat some values and also generate NaN in our DataFrame.

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import numpy as np
import pandas as pd
# creating data dictionary 
data_dict = {'col_1':[1,2,3,4,5],
           'col_2':[111,222,333,111,555],
           'col_3':['alpha','bravo','charlie',np.nan,np.nan],
           }
# Creating DataFrame from data_dict
df = pd.DataFrame(data_dict,index=[1,2,3,4,5])
print(df) # let's have a look at what the DataFrame looks like!

The info() method

The info() method provides a concise summary of a DataFrame. We’ll use this function often in the course.

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print(df.info())

The head(n) method

The head(n) method returns the first n rows of a DataFrame. Its default value is 5. This method is very useful for getting an overview of our data. We’ll use this often in the course.

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#Default Call
print(df.head())
print()
#Call with parameter
print(df.head(2))

The isnull() method

The isnull() method returns a same-sized boolean object that indicates whether the values are null or not.

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print(df.isnull())

The dropna() method

The dropna() method removes the rows that contains NULL values.

By default, this method works with the following conditions in place:

  • axis = 0/rows, 1/columns – 0 is default
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