Intro To Pandas

One of the most common python libraries used by data scientists.

Because it can connect to just about any data source, such as SQL database, from web, load from excel and much more

Pandas provide easy-to-use data structures and tools for effectively loading, manipulating and exporting in-memory data in python

The pandas library helps you explore your data and visually see the structure of your output as you are transforming your data.

Sickit-learn, statsmodels and data visualization libraries( matplotib, seaborn)

Let’s you quickly visualize your data or feed it into a machine learning algorithm

Data presented in columns or tables

To access just certain parts of our dataset

With square bracketz, single brackets return a panda series and double brackets return dataframe

The essential difference is how they are indexed

from google.colab import drive drive.mount(‘/content/drive’)

df[ ‘Mortgage Name’ ]

df[’ Mortgage Name’ ].value_counts()

df[df[ ‘Mortgage Name’ ] == ‘30 Year’s]

df = df.loc[mortgage_filter & interest_filter, :] df

~

df = df.rename(columns={‘Starting Balance’: ‘starting_balance’})

df = df.drop(columns=[‘new_balnce’]) df.head() Or del df[‘starting_balance’]

df.duplicated() to check for duplicates And to count them use df.duplicated().sum()

df - df.drop_duplicates() df.duplicated().sum()

df = df.drop(columns=[‘unamed: 0’, ‘passengerId’])

Tells us how many unique values are in each room, finding relationships between the column value and other data

Tells us a lot about our data. Checks columns, rows, data types, and missing values

df.[‘sibsp’] = df[‘sibsp’].replace(‘one, 1) df[‘sibs’] = df[‘sibsp’].astype(int) df.info()