Dataframes - Part 01

Summary

This tutorial introduces the Pandas library in Python, widely used for data manipulation and analysis. It begins with instructions on installing and importing Pandas, followed by an explanation of DataFrames, a key object that functions like a table similar to Excel or SQL. The tutorial demonstrates how to load data from various sources like CSV files and SQL databases, and provides a hands-on example of reading and manipulating a CSV file. Additionally, it touches on the integration of Pandas with machine learning libraries like Scikit-learn for data analysis and modeling.

Takeaways

• 🐍 Python's pandas library is essential for data manipulation.
• 📚 You can install pandas via pip if it's not included with Python.
• 🔌 Importing pandas is done using 'import pandas as pd'.
• 📊 A DataFrame is the primary object in pandas, akin to a spreadsheet or SQL table.
• 💾 DataFrames are optimized for data aggregation and calculations.
• 🔄 DataFrames can be inputted into machine learning models, not just arrays.
• 🌐 Data can be sourced from various formats like CSV, Excel, SQL, and APIs.
• 📁 The script demonstrates how to read a CSV file into a DataFrame.
• 🔍 The script shows how to specify the path to a file for data reading.
• 📈 The script also touches on loading datasets from libraries like scikit-learn.

Q & A

What is pandas and why is it important for data manipulation?

Pandas is a Python library that provides data structures and data analysis tools for Python programs. It is widely used for data manipulation and analysis because it allows for efficient and easy handling of structured data.

How can you install pandas if it's not already available in your Python environment?

You can install pandas via the terminal using the command 'pip install pandas'.

What is the typical way to import pandas in a Python script?

The typical way to import pandas is by using the line 'import pandas as pd'.

What is a DataFrame in pandas?

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It is similar to a table or a spreadsheet and is the primary data structure used in pandas.

How does pandas relate to Excel and SQL?

Pandas can perform operations similar to both Excel and SQL. It allows for data manipulation like Excel and can execute operations similar to SQL queries.

Can pandas DataFrames be used in machine learning models?

Yes, pandas DataFrames can be used as input for machine learning models. You can provide the DataFrame as an entry instead of an array.

What are some different data sources from which pandas can read data?

Pandas can read data from various sources including CSV files, APIs, SQL queries, Excel files, and clipboard.

How do you read a CSV file into a pandas DataFrame?

You can read a CSV file into a DataFrame using the function 'pd.read_csv(filepath)' where 'filepath' is the location of the CSV file.

What does the 'pd.read_csv()' function do?

The 'pd.read_csv()' function reads a comma-separated values (CSV) file into a pandas DataFrame.

How can you specify the separator in a CSV file when reading it into a DataFrame?

You can specify the separator in a CSV file by using the 'sep' parameter in the 'pd.read_csv()' function. For example, if the separator is a tab, you would use 'pd.read_csv(filepath, sep='\t')'.

What does the 'pd.read_csv()' function return?

The 'pd.read_csv()' function returns a DataFrame object containing the data from the CSV file.

Can you provide an example of how to load a dataset from a machine learning library using pandas?

Yes, you can load datasets from libraries like scikit-learn using pandas. For example, you can load the diabetes dataset from scikit-learn using 'from sklearn.datasets import load_diabetes' and then convert it to a DataFrame.

Outlines

00:00

🐍 Introduction to Pandas

The speaker introduces the audience to the Pandas library in Python, which is used for data manipulation. They explain that Pandas is typically installed with Python, but can be installed via terminal using the command 'pip install pandas'. The main object in Pandas is the DataFrame, which is likened to a table and can be used for operations similar to those in Excel or SQL. The speaker emphasizes the practicality and widespread use of DataFrames in various programming languages like R, and mentions their utility in machine learning and regression tasks. They also touch on the ability to read data from various sources such as CSV, APIs, SQL, and Excel files.

05:03

📂 Reading Data into DataFrames

The speaker demonstrates how to read data from a CSV file into a DataFrame. They guide through the process of creating a Google Sheet, downloading it as a CSV, and then moving and renaming the file for easier access. The path to the CSV file is identified, and the speaker shows how to use the 'pd.read_csv()' function in Pandas to load the data into a DataFrame. The DataFrame structure is explained, including headers and index columns, and the speaker saves the loaded content into a variable named 'restaurants'. They also mention the ability to read data from other file types and sources like Google BigQuery, JSON, and pickle files.

10:04

📊 Loading Datasets in Pandas

The speaker discusses the process of loading datasets into Pandas, specifically mentioning the 'seaborn' library, which is used for machine learning. They explain how to access different datasets provided by the library and load them using the 'load_dataset()' function. The focus is on working with a specific dataset, and the speaker provides a brief overview of the datasets available. The music in the background suggests an informal and engaging tone to the tutorial.

Keywords

💡pandas

Pandas is an open-source Python library used for data manipulation and analysis. It provides data structures and functions needed for manipulating numerical tables and time series. In the video, it's mentioned as a widely used library for working with data frames, which are essential for data analysis tasks. The script describes how to install pandas using pip and import it into a Python environment.

💡data frame

A data frame is a two-dimensional labeled data structure with columns of potentially different types. It is the primary data structure used in pandas and is similar to a table in a spreadsheet or a SQL table. The video explains that data frames can be manipulated to perform operations akin to those in Excel or SQL, making them a versatile tool for data analysis.

💡CSV

CSV stands for Comma-Separated Values and is a file format that stores tabular data, either in plain text or in a spreadsheet. In the script, the process of reading data from a CSV file using pandas is described. The CSV file is used as an example to demonstrate how pandas can be used to load data into a data frame.

💡install

The term 'install' refers to the process of setting up a software or library on a computer system so that it can be used. In the context of the video, it's mentioned that pandas is often installed with Python, but if not, it can be installed via the terminal using the command 'pip install pandas'.

💡terminal

A terminal is a type of computer program that allows users to interact with the computer through typed commands. In the video, the terminal is used to demonstrate how to install the pandas library by running the command 'pip install pandas'.

💡import

In Python, 'import' is a statement used to include modules or libraries in your code so that you can use their functions. The script shows how to import pandas as 'pd', which is a common practice to make the code shorter and more readable.

💡read

In the context of pandas, 'read' refers to the function used to load data from various file formats into a data frame. The video script describes how to use 'pd.read_csv()' to read data from a CSV file and load it into a data frame.

💡Google Sheets

Google Sheets is a web-based spreadsheet program that allows users to create, edit, share, and collaborate on spreadsheets online. The video script mentions creating a Google Sheet to demonstrate how data can be exported as a CSV file and then read into a pandas data frame.

💡SQL

SQL, or Structured Query Language, is a domain-specific language used in programming and software engineering as a standard language for managing data held in a relational database management system. The video mentions establishing a connection with SQL to read data into a pandas data frame.

💡machine learning

Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The script suggests that pandas data frames can be used as input for machine learning models, indicating the library's utility beyond just data manipulation.

💡regression

Regression is a statistical method used to determine the strength and character of the relationship between one dependent variable and a series of independent variables. The video script briefly mentions that pandas data frames can be used for regression analysis, highlighting the library's role in statistical modeling.

Highlights

Introduction to pandas as a Python library for data manipulation.

How to install pandas via terminal using the command 'pip install pandas'.

Importing pandas in Python with 'import pandas as pd'.

Pandas is widely used for working with data in Python.

DataFrame is the main object manipulated in pandas, similar to a table.

Pandas allows for both SQL-like and Excel-like operations on data.

DataFrames are practical for data aggregation and calculations.

DataFrames can be used as inputs for machine learning libraries.

Pandas can read data from various sources like CSV, APIs, SQL, etc.

Demonstration of reading data from a CSV file.

Explanation of how to specify the correct file path for data reading.

Renaming files for easier reference when working with data.

Using the 'pd.read_csv()' function to load CSV data into a DataFrame.

The importance of specifying the correct separator in the read function.

Visual representation of how data is loaded into a DataFrame.

Different sources from which pandas can read data, including Google BigQuery and JSON files.

Loading datasets from the 'seaborn' library for analysis.

Overview of the various datasets available for loading in pandas.

Transcripts

[Music]

hello everyone and welcome to the part

of data frame in pandas so uh pandas is

a library in Python and this library is

enabled us to getting all data in a nice

way

um so how does it work and what do we

need so we need to import this Library

called pandas

I normally it's like installed when we

installed python but you might need to

install it via the terminal so to

install it viazo terminal you open your

terminal and you do this command pip

install pandas so first try to run this

line in your notebook this import pandas

aspd so this is uh what we're going to

do so here we do

um import

pandas

aspd

so this is what we want to do and this

works right so if you have a mirror then

you will open your terminal

you will open your terminal uh in your

terminal I'm opening it

uh in your terminal uh you will do

something like peeping Style

Pippin style pandas if you need uh so I

don't need to did it I don't need to do

it so I'm not running this command so

pandas is a library for data frame it is

the main library that is used in Python

widely to work with data

um so the main objects that we

manipulate when we work with pandas is

called a data frame a data frame is a

bit like a table and on this table you

can do operation as a parallel that we

can draw uh pandas will work a bit like

an Excel table

uh so it's a mix I would say between

Excel and SQL where you can do some SQL

like operation and you can do some Excel

operation so how does it look like so uh

pandas is a way to manipulate data Frame

data frame is an object you will tell me

okay it's an object but what kind of

object so it is an object that is a

quite particular right

so it is um an object but in Python

everything is object and it enables us

to work with tables in Python it is very

practical it is also widely used so it

is a type of data that is also used if

you work in another programming language

let's say R you will also manipulate

data frame in R and it's very nice

because there is a lot of functions that

are already optimized to work with this

kind of data so if you want to do some

aggregation you want to do some

calculation on your data this is very

nicely optimized with data frame

um and uh let's say you were working

with library to do machine learning to

do your regression Etc you can also

provide this data frame as an entry so

widely uh if you use psychic learn or

for like machine learning or you use

like uh other stuff to do regression

like we've seen before this type model

you can enter instead of array you can

enter the colon of your data frame as

inputs so this is also some things that

is possible with data frame

um so yes uh we need to get data uh but

from where you know so uh there is

different kind of data that exist right

you can get data from internet you can

have data saving an Excel file etc etc

um so we've append us we are able uh to

read data from different sources so we

can read data from CSV from an API from

SQL clipboard etc etc so I'm going to

demonstrate uh or you can do all this

kind of different stuff so if you manage

to establish a connection with SQL then

you can read data from some SQL queries

right

I'm just going to show you how it works

um with like digged uh etc etc so

um

um this is how it looks and uh yeah so

pandas is looking like PD so I can do

like PD dots um

from PD Dot read so if I do PD read I do

see I read CSV I read my Excel Etc so if

I have a csvs and I will put the path to

my stuff so if I have let's say

table.csv somewhere table dot CSV

somewhere it will work so we can do this

together so let's say here I have a

jamboard but what I will go I will go to

my drive

drive and I will create a Google sheet I

could create a Google chat

um so I will have you let's say we're

going to go back with a restaurant uh

and I have restaurant uh one up and then

I will have a city and I will have Paris

and then I have uh restaurants two and

three that's it and then I will have

let's say my rating or here I have like

three four and five and here you have

Paris London

uh in room

okay so we have that I know I give it a

name I call it like restaurants

restaurants up I know I can do download

right so I can download a CSV

uh so I'm downloading my CSV it's called

restaurant right uh and now uh I can go

in my download and I know it is there

right so I know it is uh in my download

uh so what I'm gonna do uh from my

download to I'm gonna move it

um I'm gonna uh check what is a path of

it so I know like uh my new restaurant

here it has a certain path so I'm

looking to my path I know it's like oh

uh Macintosh

I'm like okay cool

um so no I'm go there and I can copy

paste this link so I know I go to like

users

uh Morgan

download and it's called Uh

the restaurant I'm going to really meet

restaurants you know so it's like easier

because it has a weird name so I'm just

gonna rename it like restaurants dot CSV

so this is I just remembering renaming

renaming my stuff

use.csv uh so yeah I have my CSV and

it's called restaurants restaurants

[Music]

torrents.csp

so I go do something something like this

oh you have to go back to like users and

stuff

um yeah so we need to like specify uh

the right path uh Etc

um yeah so this is uh how um it will uh

work basically you will specify your

file I would just copy it

in my uh where I got it and here I can

now open my restaurants

yep so here it works you know uh and I

do see that aperture tabulation so I can

put a step and I think I need to put tab

or something like yeah t

things this is how we do uh yeah so you

know I choose to download a CSV that was

with a separation with tabulation but if

I have a CSV which separation is a coma

I will put this like in this function

the separator but in my case I go to

tabulation so I'm using this and you do

see here it looks a bit like the stuff I

have online right so here is like my

headers

oh my headers

yeah you have like three column but I

also have like three rows and here I

have like zero one two and it's like

zero one two correspond to not truly to

this but it's a bit the same so it's

like an index so this will be equal to

Index this would be called My First

Column second column third column there

are three rows so this will be my um

table so I could be like DF uh it's to

data frame we usually call them DF and

if I know to like GF restaurants it's

that one so I can save the content of

this I just read it I save it in a

variable uh I know I have it here as a

GF restaurant so this is where I reread

it from CSV and uh I've seen before you

know when we do read uh we do see like

this is for Google bigquery this is from

a Json file this is from a pickle file

see from s is query table whatever

XML stutter so you have all the

different sources and you have even more

available

so now we're gonna work uh with data

sets so it's like some data sets that

already exist uh so we're just going to

do uh from Escalon

that's it

that's it's important yeah

from SQL and imported as a victim I can

do this so I can do this and then I can

do dataset dot lot uh David so you have

like is this is just to lot something uh

and then I can do di figure a lot

diabetes so this is just

um a function

um uh where I'm just gonna learn this is

not very important it's just like I'm uh

in in this Library uh so you're gonna

see

um the app is looking like this

um so in this

um in in uh so we want to load uh

something from

um Escalade so Escalade is a machine

learning library if you want per

um Excellence uh

um and then

um there is a different data items that

are provided so if you read a year you

know if I just go to like this data set

thing if I just go to the data set and I

do dot something

um I can just see in load I have loads

um of uh different uh data sets that are

imported uh so yeah you could you could

see them here basically uh so these are

all the different possible data sets uh

and then we can load Zen basically

uh so this is how it works and we might

mainly going to work with this data set

here

[Music]