🧳 Part 1: Day 1 - 3
🧳 Part 2: Day 4 - 6
🧳 Part 3: Day 7 - 9
🧳 Part 4: Day 10 - 12
🧳 Part 5: Day 13 - 15
🧳 Part 6: Day 16 - 18
🧳 Part 7: Day 19 - 21
🧳 Part 8: Day 22 - 24
🧳 Part 9: Day 25 - 27
🧳 Part 10: Day 28 - 30

📘 Day 25
Pandas
Importing pandas
DataFrames
Reading CSV File using pandas
- Data Exploration
Modifying DataFrame
Checking data types of Column values
- Boolean Indexing
Exercises: Day 25
📘 Day 26
Python for Web
Exercises: Day 26
📘 Day 27
Python with MongoDB
MongoDB
💻 Exercises: Day 27 GIVE FEEDBACK: http://thirtydayofpython-api.herokuapp.com/feedback

📘 Day 25

Pandas

Pandas is an open source,high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas adds data structures and tools designed to work with table-like data which is Series and Data Frames Pandas provides tools for data manipulation: reshaping, merging, sorting, slicing, aggregation and imputation.

pip install conda
conda install pandas

Pandas data structure is based on Series and DataFrames A series is a column and a DataFrame is a multidimensional table made up of collection of series. In order to create a pandas series we should use numpy to create a one dimensional arrays or a python list. Let's see an example of a series:

Names pandas Series

Countries Series

Cities Series

As you can see, pandas series is just one column data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.

Let's see, an example of a pandas data frame:

Data from is a collection of rows and columns. Look at the table below it has many columns than the above

Next, we will see how to import pandas and how to create Series and DataFrames using pandas

Importing pandas

import pandas as pd # importing pandas as pd
import numpy  as np # importing numpy as np

Creating Pandas Series with default index

nums = [1, 2, 3, 4,5]
s = pd.Series(nums)
s

0    1
1    2
2    3
3    4
4    5
dtype: int64

Creating Pandas Series with custom index

nums = [1, 2, 3, 4, 5]
s = pd.Series(nums, index=[1, 2, 3, 4, 5])
s

1    1
2    2
3    3
4    4
5    5
dtype: int64

fruits = ['Orange','Banana','Mangao']
fruits = pd.Series(fruits, index=[1, 2, 3])
fruits

1    Orange
2    Banana
3    Mangao
dtype: object

Creating Pandas Series from a dictionary

dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}

s = pd.Series(dct)
s

name       Asabeneh
country     Finland
city       Helsinki
dtype: object

Creating a constant pandas series

s = pd.Series(10, index = [1, 2,3])
s

1    10
2    10
3    10
dtype: int64

Creating a pandas series using linspace

s = pd.Series(np.linspace(5, 20, 10)) # linspace(starting, end, items)
s

0     5.000000
1     6.666667
2     8.333333
3    10.000000
4    11.666667
5    13.333333
6    15.000000
7    16.666667
8    18.333333
9    20.000000
dtype: float64

DataFrames

Pandas data frames can be created in different ways.

Creating DataFrames from list of lists

data = [
    ['Asabeneh', 'Finland', 'Helsink'], 
    ['David', 'UK', 'London'],
    ['John', 'Sweden', 'Stockholm']
]
df = pd.DataFrame(data, columns=['Names','Country','City'])
df

	Names	Country	City
0	Asabeneh	Finland	Helsink
1	David	UK	London
2	John	Sweden	Stockholm

Creating DataFrame using Dictionary

data = {'Name': ['Asabeneh', 'David', 'John'], 'Country':[
    'Finland', 'UK', 'Sweden'], 'City': ['Helsiki', 'London', 'Stockholm']}
df = pd.DataFrame(data)
df

	Name	Country	City
0	Asabeneh	Finland	Helsiki
1	David	UK	London
2	John	Sweden	Stockholm

Creating DataFrams from list of dictionaries

data = [
    {'Name': 'Asabeneh', 'Country': 'Finland', 'City': 'Helsinki'},
    {'Name': 'David', 'Country': 'UK', 'City': 'London'},
    {'Name': 'John', 'Country': 'Sweden', 'City': 'Stockholm'}]
df = pd.DataFrame(data)
df

	Name	Country	City
0	Asabeneh	Finland	Helsinki
1	David	UK	London
2	John	Sweden	Stockholm

Reading CSV File using pandas

import pandas as pd

df = pd.read_csv('./data/weight-height.csv')

Data Exploration

Let's read only the first 5 rows using head()

df.head() # give five rows we can increase the number of rows by passing argument to the head() method

	Gender	Height	Weight
0	Male	73.847017	241.893563
1	Male	68.781904	162.310473
2	Male	74.110105	212.740856
3	Male	71.730978	220.042470
4	Male	69.881796	206.349801

As you can see the csv file has three rows:Gender, Height and Weight. But we don't know the number of rows. Let's use shape meathod.

df.shape # as you can see 10000 rows and three columns

(10000, 3)

Let's get all the columns using columns.

df.columns

Index(['Gender', 'Height', 'Weight'], dtype='object')

Let's read only the last 5 rows using tail()

df.tail() # tails give the last five rows, we can increase the rows by passing argument to tail method

	Gender	Height	Weight
9995	Female	66.172652	136.777454
9996	Female	67.067155	170.867906
9997	Female	63.867992	128.475319
9998	Female	69.034243	163.852461
9999	Female	61.944246	113.649103

Now, lets get specif colums using the column key

heights = df['Height'] # this is now a a series

heights

0       73.847017
1       68.781904
2       74.110105
3       71.730978
4       69.881796
          ...    
9995    66.172652
9996    67.067155
9997    63.867992
9998    69.034243
9999    61.944246
Name: Height, Length: 10000, dtype: float64

weights = df['Weight'] # this is now a series

weights

0       241.893563
1       162.310473
2       212.740856
3       220.042470
4       206.349801
           ...    
9995    136.777454
9996    170.867906
9997    128.475319
9998    163.852461
9999    113.649103
Name: Weight, Length: 10000, dtype: float64

len(heights) == len(weights)

True

heights.describe() # give statisical information about height data

count    10000.000000
mean        66.367560
std          3.847528
min         54.263133
25%         63.505620
50%         66.318070
75%         69.174262
max         78.998742
Name: Height, dtype: float64

weights.describe()

count    10000.000000
mean       161.440357
std         32.108439
min         64.700127
25%        135.818051
50%        161.212928
75%        187.169525
max        269.989699
Name: Weight, dtype: float64

df.describe()  # describe can also give statistical information from a datafrom

	Height	Weight
count	10000.000000	10000.000000
mean	66.367560	161.440357
std	3.847528	32.108439
min	54.263133	64.700127
25%	63.505620	135.818051
50%	66.318070	161.212928
75%	69.174262	187.169525
max	78.998742	269.989699

Modifying DataFrame

Modifying a DataFrame * We can create a new DataFrame * We can create a new column and add to DataFrame, * we can remove an existing column from DataFrame, * we can modify an existing column from DataFrame, * we can change the data type of column values from DataFrame

Create a DataFrame

All the time, first we import the necessary packages. Now, lets import pandas and numpy two best friends ever.

import pandas as pd
import numpy as np
data = [
    {"Name": "Asabeneh", "Country":"Finland","City":"Helsinki"},
    {"Name": "David", "Country":"UK","City":"London"},
    {"Name": "John", "Country":"Sweden","City":"Stockholm"}]
df = pd.DataFrame(data)
df

	Name	Country	City
0	Asabeneh	Finland	Helsinki
1	David	UK	London
2	John	Sweden	Stockholm

Adding column in DataFrame is like adding a key in dictionary.

First let's use the previous example to create a DataFrame. After we create the DataFrame, we will start modifying the columns and column values.

Adding new column

Let's add a weight column in the DataFrame

weights = [74, 78, 69]
df['Weight'] = weights
df

	Name	Country	City	Weight
0	Asabeneh	Finland	Helsinki	74
1	David	UK	London	78
2	John	Sweden	Stockholm	69

Let's add a height column in the DataFrame

heights = [173, 175, 169]
df['Height'] =heights
df

	Name	Country	City	Weight	Height
0	Asabeneh	Finland	Helsinki	74	173
1	David	UK	London	78	175
2	John	Sweden	Stockholm	69	169

As you can see from the above DataFrame, now we new added columns, the Weight and Height. Let's add one additional column by called BMI(Body Mass Index) by calculating their BMI using thier mass and height. BMI is mass divided by height square meter(Weight/Height * Height).

As you can see, the hieght is in centimeter, so we shoud change the height to meter. So, let's modify the height row

Modifying column values

df['Height'] = df['Height'] * 0.01
df

	Name	Country	City	Weight	Height
0	Asabeneh	Finland	Helsinki	74	1.73
1	David	UK	London	78	1.75
2	John	Sweden	Stockholm	69	1.69

# Using function makes our code clean but you can just calculate the bmi without function
def calculate_bmi ():
    weights = df['Weight']
    heights = df['Height']
    bmi = []
    for w,h in zip(weights, heights):
        b = w/(h*h)
        bmi.append(b)
    return bmi

bmi = calculate_bmi()

df['BMI'] = bmi
df

	Name	Country	City	Weight	Height	BMI
0	Asabeneh	Finland	Helsinki	74	1.73	24.725183
1	David	UK	London	78	1.75	25.469388
2	John	Sweden	Stockholm	69	1.69	24.158818

Formating DataFrame column

The BMI of the above DataFrame has is float with many significant digits after decimal. Let's make it to have only one significant digit after point.

df['BMI'] = round(df['BMI'], 1)
df

	Name	Country	City	Weight	Height	BMI
0	Asabeneh	Finland	Helsinki	74	1.73	24.7
1	David	UK	London	78	1.75	25.5
2	John	Sweden	Stockholm	69	1.69	24.2

The information in the DataFrame seems not yet complete, let's add birth year and current year columns.

birth_year = ['1769', '1985', '1990']
current_year = pd.Series(2019, index=[0, 1,2])
df['Birth Year'] = birth_year
df['Current Year'] = current_year
df

	Name	Country	City	Weight	Height	BMI	Birth Year	Current Year
0	Asabeneh	Finland	Helsinki	74	1.73	24.7	1769	2019
1	David	UK	London	78	1.75	25.5	1985	2019
2	John	Sweden	Stockholm	69	1.69	24.2	1990	2019

Checking data types of Column values

df.Weight.dtype

dtype('int64')

df['Birth Year'].dtype # it give string object , we should change this to number

dtype('O')

df['Birth Year'] = df['Birth Year'].astype('int')
df['Birth Year'].dtype # let's check the data type now

dtype('int64')

df['Current Year'] = df['Current Year'].astype('int')
df['Current Year'].dtype

dtype('int64')

Now, the column values of birth year and current year are integers. We can calculate the age.

ages = df['Current Year'] - df['Birth Year']
ages

0    250
1     34
2     29
dtype: int64

df['Ages'] = ages
df

	Name	Country	City	Weight	Height	BMI	Birth Year	Current Year	Ages
0	Asabeneh	Finland	Helsinki	74	1.73	24.7	1769	2019	250
1	David	UK	London	78	1.75	25.5	1985	2019	34
2	John	Sweden	Stockholm	69	1.69	24.2	1990	2019	29

The person in the first row lives 250 years. It is unlikely for someone to live 250 years. Either it is a typo or the data is cooked. So lets fill that data with average of the columns without including outlier.

mean = (34 + 29)/ 2

mean = (34 + 29)/ 2
mean

31.5

Boolean Indexing

df[df['Ages'] > 120]

	Name	Country	City	Weight	Height	BMI	Birth Year	Current Year	Ages
0	Asabeneh	Finland	Helsinki	74	1.73	24.7	1769	2019	250

df[df['Ages'] < 120]

	Name	Country	City	Weight	Height	BMI	Birth Year	Current Year	Ages
1	David	UK	London	78	1.75	25.5	1985	2019	34
2	John	Sweden	Stockholm	69	1.69	24.2	1990	2019	29

df['Ages']  = df[df['Ages'] > 120]

Exercises: Day 25

Read the hacker_ness.csv file from data directory
Get the first five rows
Get the last five rows
Get the title column as pandas series
Count the number of rows and columns
- Filter the titles which contain python
- Filter the titles which contain JavaScript
- Explore the data and make sense of the data

📘 Day 26

Python for Web

Python is a general purpose programming language and it can be used for many places. In this section, we will see how we use python for the web. There are many python web frame works. Django and Flask are the most popular ones. Today, we will see how to use Flask for web development.

Flask

Flask is a web development framework written in python. Flask uses Jinja2 template engine. Flask can be also used with other modern frond libraries such as react. If you did not install the virtualenv package ye install it first. Virtual environment will allows to isolate project dependencies.

Folder structure

After completing all the step your project file structure should look like this:

├── Procfile
├── app.py
├── env
│   ├── bin
├── requirements.txt
├── static
│   └── css
│       └── main.css
└── templates
    ├── about.html
    ├── home.html
    ├── layout.html
    ├── post.html
    └── result.html

Setting up your project directory

Follow, the following steps to get started with Flask. Step 1: install virtualenv using the following command.

pip install virtualenv

Step 2:

asabeneh@Asabeneh:~/Desktop$ mkdir python_for_web
asabeneh@Asabeneh:~/Desktop$ cd python_for_web/
asabeneh@Asabeneh:~/Desktop/python_for_web$ virtualenv env
asabeneh@Asabeneh:~/Desktop/python_for_web$ source env/bin/activate
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ pip freeze
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ pip install Flask
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ pip freeze
Click==7.0
Flask==1.1.1
itsdangerous==1.1.0
Jinja2==2.10.3
MarkupSafe==1.1.1
Werkzeug==0.16.0
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$

We created a project director named pythonfor_web. Inside the project we created a virtual environment _env which could be any name but I prefer to call it env. Then we activated the virtual environment. We used pip freeze to check the installed packages in the project directory. The result of pip freeze was empty because a package was not installed yet.

Now, let's create app.py file in the project directory and write the following code. The app.py file will be the main file in the project. The following code has flask module, os module.

Creating routes

The home route.

# let's import the flask
from flask import Flask
import os # importing operating system module

app = Flask(__name__)

@app.route('/') # this decorator create the home route
def home ():
    return '<h1>Welcome</h1>'

@app.route('/about')
def about():
    return '<h1>About us</h1>'


if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

After you run python app.py check local host 5000.

Let's add additional route. Creating about route

# let's import the flask
from flask import Flask
import os # importing operating system module

app = Flask(__name__)

@app.route('/') # this decorator create the home route
def home ():
    return '<h1>Welcome</h1>'

@app.route('/about')
def about():
    return '<h1>About us</h1>'

if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

Now, we added the about route in the above code. How about if we want to render an HTML file instead of string? It is possible to render HTML file using the function render_templae. Let's create a folder called templates and create home.html and about.html in the project directory. Let's also import the render_template function from flask.

Creating templates

Create the HTML files inside templates folder.

home.html

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Home</title>
  </head>

  <body>
    <h1>Welcome Home</h1>
  </body>
</html>

about.html

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>About</title>
  </head>

  <body>
    <h1>About Us</h1>
  </body>
</html>

Python Script

app.py

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

app = Flask(__name__)

@app.route('/') # this decorator create the home route
def home ():
    return render_template('home.html')

@app.route('/about')
def about():
    return render_template('about.html')

if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

As you can see to go to different pages or to navigate we need a navigation. Let's add a link to each page or let's create a layout which we use to every page.

<ul>
  <li><a href="/">Home</a></li>
  <li><a href="/about">About</a></li>
</ul>

Now, we can navigate between the pages using the above link. Let's create additional page which handle form data. You can call it any name, I like to call it post.html.

We can inject data to the HTML files using Jinja2 template engine.

# let's import the flask
from flask import Flask, render_template, request, redirect, url_for
import os # importing operating system module

app = Flask(__name__)

@app.route('/') # this decorator create the home route
def home ():
    techs = ['HTML', 'CSS', 'Flask', 'Python']
    name = '30 Days Of Python Programming'
    return render_template('home.html', techs=techs, name = name, title = 'Home')

@app.route('/about')
def about():
    name = '30 Days Of Python Programming'
    return render_template('about.html', name = name, title = 'About Us')

@app.route('/post')
def post():
    name = 'Text Analyzer'
    return render_template('post.html', name = name, title = name)


if __name__ == '__main__':
    # for deployment
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

Let's see the templates too:

home.html

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Home</title>
  </head>

  <body>
    <ul>
      <li><a href="/">Home</a></li>
      <li><a href="/about">About</a></li>
    </ul>
    <h1>Welcome to {{name}}</h1>
    {% for tech in techs %}
    <ul>
      <li>{{tech}}</li>
    </ul>
    {% endfor %}
  </body>
</html>

about.html

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>About Us</title>
  </head>

  <body>
    <ul>
      <li><a href="/">Home</a></li>
      <li><a href="/about">About</a></li>
    </ul>
    <h1>About Us</h1>
    <h2>{{name}}</h2>
  </body>
</html>

Creating a layout

In the template files, there are lots of repeated codes, we can write a layout and we can remove the repetition. Let's create layout.html inside the templates folder. After we create the layout we will import to every file.

layout.html

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <link
      href="https://fonts.googleapis.com/css?family=Lato:300,400|Nunito:300,400|Raleway:300,400,500&display=swap"
      rel="stylesheet"
    />
    <link
      rel="stylesheet"
      href="{{ url_for('static', filename='css/main.css') }}"
    />
    {% if title %}
    <title>30 Days of Python - {{ title}}</title>
    {% else %}
    <title>30 Days of Python</title>
    {% endif %}
  </head>

  <body>
    <header>
      <div class="menu-container">
        <div>
          <a class="brand-name nav-link" href="/">30DaysOfPython</a>
        </div>
        <ul class="nav-lists">
          <li class="nav-list">
            <a class="nav-link active" href="{{ url_for('home') }}">Home</a>
          </li>
          <li class="nav-list">
            <a class="nav-link active" href="{{ url_for('about') }}">About</a>
          </li>
          <li class="nav-list">
            <a class="nav-link active" href="{{ url_for('post') }}"
              >Text Analyzer</a
            >
          </li>
        </ul>
      </div>
    </header>
    <main>
      {% block content %} {% endblock %}
    </main>
  </body>
</html>

Now, lets remove all the repeated code in the other template files and import the layout.html. The href is using url_for function with the name of the route function to connect each navigation route.

home.html

{% extends 'layout.html' %} {% block content %}
<div class="container">
  <h1>Welcome to {{name}}</h1>
  <p>
    This application clean texts and analyse the number of word, characters and
    most frequent words in the text. Check it out by click text analyzer at the
    menu. You need the following technologies to build this web application:
  </p>
  <ul class="tech-lists">
    {% for tech in techs %}
    <li class="tech">{{tech}}</li>

    {% endfor %}
  </ul>
</div>

{% endblock %}

about.html

{% extends 'layout.html' %} {% block content %}
<div class="container">
  <h1>About {{name}}</h1>
  <p>
    This is a 30 days of python programming challenge. If you have been coding
    this far, you are awesome. Congratulations for the job well done!
  </p>
</div>
{% endblock %}

post.html

{% extends 'layout.html' %} {% block content %}
<div class="container">
  <h1>Text Analyzer</h1>
  <form action="https://thirtydaysofpython-v1.herokuapp.com/post" method="POST">
    <div>
      <textarea rows="25" name="content" autofocus></textarea>
    </div>
    <div>
      <input type="submit" class="btn" value="Process Text" />
    </div>
  </form>
</div>

{% endblock %}

Request methods, there are different request methods(GET, POST, PUT, DELETE) are the common request methods which allow us to do CRUD(Create Read Update Delete) operation.

In the post, route we will use GET and POST method alternative depending on the type of request, check how it looks in the code below. The request method is a function to handle request methods and also to access form data. app.py

# let's import the flask
from flask import Flask, render_template, request, redirect, url_for
import os # importing operating system module

app = Flask(__name__)
# to stop caching static file
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0



@app.route('/') # this decorator create the home route
def home ():
    techs = ['HTML', 'CSS', 'Flask', 'Python']
    name = '30 Days Of Python Programming'
    return render_template('home.html', techs=techs, name = name, title = 'Home')

@app.route('/about')
def about():
    name = '30 Days Of Python Programming'
    return render_template('about.html', name = name, title = 'About Us')

@app.route('/result')
def result():
    return render_template('result.html')

@app.route('/post', methods= ['GET','POST'])
def post():
    name = 'Text Analyzer'
    if request.method == 'GET':
         return render_template('post.html', name = name, title = name)
    if request.method =='POST':
        content = request.form['content']
        print(content)
        return redirect(url_for('result'))

if __name__ == '__main__':
    # for deployment
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

So far, we have seen how to use template and how to inject data to template, how to a common layout. Now, lets handle static file. Create a folder called static in the project director and create a folder called css. Inside css folder create main.css. Your main. css file will be linked to the layout.html.

You don't have to write the css file, copy and use it. Let's move on to deployment.

Deployment

Creating Heroku account

Heroku provides a free deployment service for both front end and fullstack applications. Create an account on heroku and install the heroku CLI for you machine. After installing heroku write the following command

asabeneh@Asabeneh:~$ heroku login
heroku: Press any key to open up the browser to login or q to exit:

Let's see the result by clicking any key from the keyboard. When you press any key from you keyboard it will open the heroku login page and click the login page. Then you will local machine will be connected to the remote heroku server. If you are connected to remote server, you will see this.

asabeneh@Asabeneh:~$ heroku login
heroku: Press any key to open up the browser to login or q to exit:
Opening browser to https://cli-auth.heroku.com/auth/browser/be12987c-583a-4458-a2c2-ba2ce7f41610
Logging in... done
Logged in as asabeneh@gmail.com
asabeneh@Asabeneh:~$

Create requirements and Procfile

Before we push our code to remote server, we need requirements

requirements.txt
Procfile

(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ pip freeze
Click==7.0
Flask==1.1.1
itsdangerous==1.1.0
Jinja2==2.10.3
MarkupSafe==1.1.1
Werkzeug==0.16.0
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ touch requirements.txt
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ pip freeze > requirements.txt
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ cat requirements.txt
Click==7.0
Flask==1.1.1
itsdangerous==1.1.0
Jinja2==2.10.3
MarkupSafe==1.1.1
Werkzeug==0.16.0
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ touch Procfile
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$ ls
Procfile          env/              static/
app.py            requirements.txt  templates/
(env) asabeneh@Asabeneh:~/Desktop/python_for_web$

The Procfile will have the command which run the application in the web server in our case on Heroku.

web: python app.py

Pushing project to heroku

Now, it is ready to be deployed. Steps to deploy the application on heroku

git init
git add .
git commit -m "commit message"
heroku create 'name of the app as one word'
git push heroku master
heroku open(to launch the deployed application)

After this step you will get an application like this

Exercises: Day 26

You will build this application. Only the text analyser part is left

📘 Day 27

Python with MongoDB

Python is a backend technology and it can be connected with different data base applications such as MongoDB and SQL.

MongoDB

MongoDB is a NoSQL database. MongoDB stores data in a JSON like document which make MongoDB very flexible and scalable. Let's see the different terminologies of SQL and NoSQL databases. The following table will make the difference between SQL vs NoSQL databases.

SQL versus NoSQL

In this section we will focus on a NoSQL database MongoDB. Lets sign up on mongoDB by click on the sign in button then click register on the next page.

Complete the fields and click continue

Select the free plan

Choose the proximate free region and give any name for you cluster.

Now, a free sandbox is created

All local host access

Add user and password

Create a mongoDB uri link

Select python 3.6 or above driver

Getting Connection String(MongoDB URI)

Copy the connection string only link and you get something like this

mongodb+srv://asabeneh:<password>@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority

Don't worry about the url, it is a means to connect your application with mongoDB. Let's replace the password placeholder with the passed you use to add a user. Example:

mongodb+srv://asabeneh:123123123@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority

Now, I replaced everything and the password is 123123 and the name of the database is thirty_days_python. This is just an example, your password must a bit strong than this.

Python needs a mongoDB driver to access mongoDB database. We will use pymongo with dnspython to connect our application with mongoDB base . Inside your project directory install pymongo and dnspython.

pip install pymongo dnspython

The "dnspython" module must be installed to use mongodb+srv:// URIs. The dnspython is a DNS toolkit for Python. It supports almost all record types.

Connecting Flask application to MongoDB Cluster

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module
MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
print(client.list_database_names())

app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

When we run the above code we get the default mongoDB databases.

['admin', 'local']

Creating a database and collection

Let's create a database, database and collection in mongoDB will be created if it doesn't exist. Let's create a data base name thirty_days_of_python and students collection. To create a database

db = client.name_of_databse # we can create a database like this or the second way
db = client['name_of_database']

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module
MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
# Creating database
db = client.thirty_days_of_python
# Creating students collection and inserting a document
db.students.insert_one({'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 250})
print(client.list_database_names())

app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

After we create a database, we also created a students collection and we used insert_one method to insert a document. Now, the data thirty_days_of_python and students collection have been created and the document has been inserted. Check your mongoDB cluster and you will see both the database and the collection. Inside the collection, there will be a document.

['thirty_days_of_python', 'admin', 'local']

If you see this on the mongoDB cluster, it means you have successfully created a database and a collection.

If you have seen on the figure, the document has been created with a long id which acts as a primary key. Every time we create a document mongoDB create and unique id for it.

Inserting many documents to collection

The insert_one() method inserts one item at a time if we want to insert many documents at once either we use insert_many() method or for loop. We can use for loop to inset many documents at once.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module
MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)

students = [
        {'name':'David','country':'UK','city':'London','age':34},
        {'name':'John','country':'Sweden','city':'Stockholm','age':28},
        {'name':'Sami','country':'Finland','city':'Helsinki','age':25},
    ]
for student in students:
    db.students.insert_one(student)


app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

MongoDB Find

The find and findOne methods common method to find data in a collection in mongoDB database. It is similar to the SELECT statement in a MySQL database. Let's use the find_one() method to get documents in the database collection.

*find_one({"_id": ObjectId("id"}): Gets the first occurrence if an id is not provided

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module
MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
student = db.students.find_one()
print(student)


app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Helsinki', 'city': 'Helsinki', 'age': 250}

The above query returns the first entry but we can target specific document using specific _id. Let's do one example, let's use David's id to get David object. '_id':ObjectId('5df68a23f106fe2d315bbc8c')

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module
from bson.objectid import ObjectId # id object
MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
student = db.students.find_one({'_id':ObjectId('5df68a23f106fe2d315bbc8c')})
print(student)

app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a23f106fe2d315bbc8c'), 'name': 'David', 'country': 'UK', 'city': 'London', 'age': 34}

We have seen, how to use find_one() using the above examples. Let's move one to find()

find(): returns all the occurrence from a collection if we don't pass a query object. The object is pymongo.cursor object.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
students = db.students.find()
for student in students:
    print(student)

app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Helsinki', 'city': 'Helsinki', 'age': 250}
{'_id': ObjectId('5df68a23f106fe2d315bbc8c'), 'name': 'David', 'country': 'UK', 'city': 'London', 'age': 34}
{'_id': ObjectId('5df68a23f106fe2d315bbc8d'), 'name': 'John', 'country': 'Sweden', 'city': 'Stockholm', 'age': 28}
{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}

We can specify which fields to return by passing second object in the find({}, {}). 0 means not include and 1 means include but we can not mix 0 and 1, except for _id.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
students = db.students.find({}, {"_id":0,  "name": 1, "country":1}) # 0 means not include and 1 means include
for student in students:
    print(student)

app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'name': 'Asabeneh', 'country': 'Finland'}
{'name': 'David', 'country': 'UK'}
{'name': 'John', 'country': 'Sweden'}
{'name': 'Sami', 'country': 'Finland'}

Find with Query

In mongoDB find take a query object. We can pass a query object and we can filter the documents we like to filter out.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
students = db.students.find(query)

for student in students:
    print(student)


app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 250}
{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}

Query with modifiers

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
students = db.students.find(query)

for student in students:
    print(student)


app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 250}
{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}

Find query with modifier

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
students = db.students.find(query)

for student in students:
    print(student)


app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 250}
{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}

Query with modifiers

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
query = {"age":{"$gt":30}}
students = db.students.find(query)
for student in students:
    print(student)


app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 250}
{'_id': ObjectId('5df68a23f106fe2d315bbc8c'), 'name': 'David', 'country': 'UK', 'city': 'London', 'age': 34}

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
query = {"age":{"$gt":30}}
students = db.students.find(query)
for student in students:
    print(student)

{'_id': ObjectId('5df68a23f106fe2d315bbc8d'), 'name': 'John', 'country': 'Sweden', 'city': 'Stockholm', 'age': 28}
{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}

Limiting documents

We can limit the number of documents we return using the limit() method.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
db.students.find().limit(3)

Find with sort

By default, sort is in ascending order. We can change to descending by adding -1 parameter.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
students = db.students.find().sort('name')
for student in students:
    print(student)


students = db.students.find().sort('name',-1)
for student in students:
    print(student)

students = db.students.find().sort('age')
for student in students:
    print(student)

students = db.students.find().sort('age',-1)
for student in students:
    print(student)

app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

Ascending order

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 250}
{'_id': ObjectId('5df68a23f106fe2d315bbc8c'), 'name': 'David', 'country': 'UK', 'city': 'London', 'age': 34}
{'_id': ObjectId('5df68a23f106fe2d315bbc8d'), 'name': 'John', 'country': 'Sweden', 'city': 'Stockholm', 'age': 28}
{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}

Descending order

{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}
{'_id': ObjectId('5df68a23f106fe2d315bbc8d'), 'name': 'John', 'country': 'Sweden', 'city': 'Stockholm', 'age': 28}
{'_id': ObjectId('5df68a23f106fe2d315bbc8c'), 'name': 'David', 'country': 'UK', 'city': 'London', 'age': 34}
{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 250}

Update with query

We will use update_one() method to update one item. It takes two object one is a qeury and the second is the new object. The first person, Asabeneh got a very implausible age. Let's update Asabeneh's age.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database

query = {'age':250}
new_value = {'$set':{'age':38}}

db.students.update_one(query, new_value)
# lets check the result if the age is modified
for student in db.students.find():
    print(student)


app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 38}
{'_id': ObjectId('5df68a23f106fe2d315bbc8c'), 'name': 'David', 'country': 'UK', 'city': 'London', 'age': 34}
{'_id': ObjectId('5df68a23f106fe2d315bbc8d'), 'name': 'John', 'country': 'Sweden', 'city': 'Stockholm', 'age': 28}
{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}

When we want to update many documents at once we use upate_many()method.

Delete Document

The method delete_one() delete one document.The delete_one() take a query object parameter. It only removes the first occurrence. Let's remove one John from the collection.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database

query = {'name':'John'}
db.students.delete_one(query)

for student in db.students.find():
    print(student)
# lets check the result if the age is modified
for student in db.students.find():
    print(student)


app = Flask(__name__)
if __name__ == '__main__':
    # for deployment we use the environ
    # to make it work for both production and development
    port = int(os.environ.get("PORT", 5000))
    app.run(debug=True, host='0.0.0.0', port=port)

{'_id': ObjectId('5df68a21f106fe2d315bbc8b'), 'name': 'Asabeneh', 'country': 'Finland', 'city': 'Helsinki', 'age': 38}
{'_id': ObjectId('5df68a23f106fe2d315bbc8c'), 'name': 'David', 'country': 'UK', 'city': 'London', 'age': 34}
{'_id': ObjectId('5df68a23f106fe2d315bbc8e'), 'name': 'Sami', 'country': 'Finland', 'city': 'Helsinki', 'age': 25}

As you can see John as been removed from the collection

When we want to delete many documents we use delete_many() method, it takes a query object. If we pass an empyt query object to delete_many({}) it will delete all the documents in the collection.

Drop a collection

Using the drop() method we can delete a collection from a database.

# let's import the flask
from flask import Flask, render_template
import os # importing operating system module

MONGODB_URI = 'mongodb+srv://asabeneh:your_password_goes_here@30daysofpython-twxkr.mongodb.net/test?retryWrites=true&w=majority'
client = pymongo.MongoClient(MONGODB_URI)
db = client['thirty_days_of_python'] # accessing the database
db.students.drop()

Now, we have deleted the students collection from the database.

💻 Exercises: Day 27

<< Part 8 | Part 10 >>

📘 Day 25

Pandas

Importing pandas

Creating Pandas Series with default index

Creating Pandas Series with custom index

Creating Pandas Series from a dictionary

Creating a constant pandas series

Creating a pandas series using linspace

DataFrames

Creating DataFrames from list of lists

Creating DataFrame using Dictionary

Creating DataFrams from list of dictionaries

Reading CSV File using pandas

Data Exploration

Modifying DataFrame

Create a DataFrame

Adding new column

Modifying column values

Formating DataFrame column

Checking data types of Column values

Boolean Indexing

Exercises: Day 25

📘 Day 26

Python for Web

Flask

Folder structure

Setting up your project directory

Creating routes

Creating templates

Python Script

Navigation

Creating a layout

Deployment

Creating Heroku account

Login to Heroku

Create requirements and Procfile

Pushing project to heroku

Exercises: Day 26

📘 Day 27

Python with MongoDB

MongoDB

SQL versus NoSQL

Getting Connection String(MongoDB URI)

Connecting Flask application to MongoDB Cluster

Creating a database and collection

Inserting many documents to collection

MongoDB Find

Find with Query

Find query with modifier

Limiting documents

Find with sort

Update with query

Delete Document

Drop a collection

💻 Exercises: Day 27