Getting started with pandas (practical example) 2021

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.

What does that even mean?

Lets get practical. We will be doing the following.

1. Get a few python list
2. Set up the data in clean way
3. Export the data to an excel sheet

Clean up raw data

Lets take some random data. We will make two list number and email

number = []
email = []

data = [
    {
        'numberrange': "53262",
        'email':'eu@aol.com',
    },
    {
        'numberrange': "553343",
        'email': "non.hendrerit.id@google.ca"
    },
    {
        'numberrange': "638442",
        'email': "donec.tempus.lorem@google.couk"
    },
    {
        'numberrange': "75523",
        'email': "lorem.vitae.odio@aol.org"
    },
    {
        'numberrange': "66493",
        'email': "orci.lacus@aol.edu"
    }
]

Looping the data

Now lets loop the data and get all instances of 'numberrange' and 'email'. We will append the results to our list we made above.

for i in data:
    print(i['numberrange'])
    print(i['email'])
    number.append(i['numberrange'])
    email.append(i['email'])

Putting it all together

import pandas as pd

number = []
email = []




data = [
    {
        'numberrange': "53262",
        'email':'eu@aol.com',
    },
    {
        'numberrange': "553343",
        'email': "non.hendrerit.id@google.ca"
    },
    {
        'numberrange': "638442",
        'email': "donec.tempus.lorem@google.couk"
    },
    {
        'numberrange': "75523",
        'email': "lorem.vitae.odio@aol.org"
    },
    {
        'numberrange': "66493",
        'email': "orci.lacus@aol.edu"
    }
]


for i in data:
    print(i['numberrange'])
    print(i['email'])
    number.append(i['numberrange'])
    email.append(i['email'])



df = pd.DataFrame()

df['Number'] = number
df['Email'] = email



# Converting to excel
df.to_excel('Make_an_excel_sheet.xlsx', index=False)