Getting started with Pandas - Letsprogram

Before you use pandas install it in your PC depending on your environment you are working. If you are using anaconda or miniconda use conda install pandas in the respective interpreter. If you use normal python IDE the download and install pandas using pip by the command pip install pandas. If you had any difficulties in installing the pandas check this link: installing pandas. 

After installing it in your PC import the pandas package into your program or interpreter.

>>>import pandas as pd

pd is just an alias for pandas instead of typing the whole name and it is assumed as a standard practice.

pandas is a fast, powerful, flexible, and easy to use open-source data analysis and manipulation tool, built on top of the Python programming language.

we now know that pandas is a data manipulation tool but the question is

what kind of data do pandas handle?

When working with tabular data, such as data stored in spreadsheets or databases, Pandas is the right tool for you. Pandas will help you to explore, clean, and process your data. In Pandas, a data table is called a DataFrame.

Pandas DataFrame Representation

I want to store the data of some movies I know the name of the movie, year released, and director.

	>>> import pandas as pd
	>>> df = pd.DataFrame({"Name":['The Godfather','The Dark Knight','Fight Club','The matrix'],
	"Year":[1972,2008,1999,1999],
	"Director":['Ford Coppola','Christopher Nolan','David Fincher','Lana and lilly']})
	>>> df
	Name Year Director
	0 The Godfather 1972 Ford Coppola
	1 The Dark Knight 2008 Christopher Nolan
	2 Fight Club 1999 David Fincher
	3 The matrix 1999 Lana and lilly
	>>> df['Year']
	0 1972
	1 2008
	2 1999
	3 1999
	Name: Year, dtype: int64
	>>> df["Name"]
	0 The Godfather
	1 The Dark Knight
	2 Fight Club
	3 The matrix
	Name: Name, dtype: object
	>>>

view raw pandas_df.py hosted with ❤ by GitHub

When using the Python dictionary here keys will be used as columns and values will be used as rows of the data frame.

When selecting the single column from the pandas DataFrame the result will be Series. The selecting of a single column from the python dictionary is the same as the pandas DataFrame here.

You can create a single column or Series from first.

>>>ages=pd.Series([19,16,32,29],name="age")

>>>ages

0   19

1   16

2    32

3    29

Name: age,  dtype: int64

We stored the movie's information in df to get the lasted movie from all the collection of movies in the data we need to do something on the DataFrame.

>>>df["year"].max( )

2008

The latest movie was released in the year 2008. Not only max pandas will also provide you many functionalities.

If you need basic statistics of a numerical table in the DataFrame use describe( ) method. df.describe( )

Note

This is just a starting point. Similar to spreadsheet software, pandas represent data as a table with columns and rows. Apart from the representation, also the data manipulations and calculations you would do in spreadsheet software are supported by pandas. Continue reading the next tutorials to get started!

How to read and write data

I had a CSV file with data I had to read it to pandas DataFrame.  pandas gave read_csv( ) method to the operations on the file. pandas also support the other file formats like SQL, Excel, JSON, etc. each have a method with the prefix 

read_*( ) 

	>>> import pandas as pd
	>>> titanic = pd.read_csv("titanic.csv")
	>>> titanic
	PassengerId Survived Pclass ... Fare Cabin Embarked
	0 1 0 3 ... 7.2500 NaN S
	1 2 1 1 ... 71.2833 C85 C
	2 3 1 3 ... 7.9250 NaN S
	3 4 1 1 ... 53.1000 C123 S
	4 5 0 3 ... 8.0500 NaN S
	.. ... ... ... ... ... ... ...
	886 887 0 2 ... 13.0000 NaN S
	887 888 1 1 ... 30.0000 B42 S
	888 889 0 3 ... 23.4500 NaN S
	889 890 1 1 ... 30.0000 C148 C
	890 891 0 3 ... 7.7500 NaN Q
	[891 rows x 12 columns]
	>>> titanic.head(5)
	PassengerId Survived Pclass ... Fare Cabin Embarked
	0 1 0 3 ... 7.2500 NaN S
	1 2 1 1 ... 71.2833 C85 C
	2 3 1 3 ... 7.9250 NaN S
	3 4 1 1 ... 53.1000 C123 S
	4 5 0 3 ... 8.0500 NaN S
	[5 rows x 12 columns]
	>>> titanic.tail(10)
	PassengerId Survived Pclass ... Fare Cabin Embarked
	881 882 0 3 ... 7.8958 NaN S
	882 883 0 3 ... 10.5167 NaN S
	883 884 0 2 ... 10.5000 NaN S
	884 885 0 3 ... 7.0500 NaN S
	885 886 0 3 ... 29.1250 NaN Q
	886 887 0 2 ... 13.0000 NaN S
	887 888 1 1 ... 30.0000 B42 S
	888 889 0 3 ... 23.4500 NaN S
	889 890 1 1 ... 30.0000 C148 C
	890 891 0 3 ... 7.7500 NaN Q
	[10 rows x 12 columns]
	>>> titanic.dtypes
	PassengerId int64
	Survived int64
	Pclass int64
	Name object
	Sex object
	Age float64
	SibSp int64
	Parch int64
	Ticket object
	Fare float64
	Cabin object
	Embarked object
	dtype: object
	>>>

view raw pandas_read.py hosted with ❤ by GitHub

If you want to read first n columns use the head( ) method or to read last n columns use tail( ) method. The code written in the above program requires a titanic.csv file. Download the titanic.csv file.

You can get the datatypes details of all Series in the DataFrame by the attribute dtypes in the pandas.

You read the data from a CSV file to pandas DataFrames now you have to extract the data as your useful format. While read_*( ) methods read the data to_*( ) extract the data from the pandas. To know more about the reading and writing methods check this link: pandas_methods.

>>>titanic.to_excel('titianic.xlsv', sheet_name="passengers")

>>> titanic.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

If you want to know more than datatypes then we use info( ) method on the DataFrame. It provides you index value, Exact RAM used to hold the DataFrame as well. We can know the missing value numbers in the File. The info method is used to know the technical information about the data.

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