What is Data Analysis?
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
In this article, I have used Pandas to know more about doing data analysis.
Mainly pandas have two data structures, series, data frames, and Panel.

Installation
The easiest way to install pandas is to use pip:

or, Download it from here.

• pandas Series

pandas series can be used for the one-dimensional labeled array.

Labels can be accessed using index attribute
print(a.index)

You can use array indexing or labels to access data in the series.
You can use array indexing or labels to access data in the series
print(a[1])
print(a[‘test4’])

You can also apply mathematical operations on pandas series.
b = a * 2
c = a ** 1.5
print(b)
print(c)

You can even create a series of heterogeneous data.
s = pd.Series([‘test1’, 1.2, 3, ‘test2’], index=[‘test3’, ‘test4’, 2, ‘4.3’])

print(s)

• pandas DataFrame

pandas DataFrame is a two-dimensional array with heterogeneous data.i.e., data is aligned in a tabular fashion in rows and columns.
Structure
Let us assume that we are creating a data frame with the student’s data.

Name	Age	Gender	Rating
Steve	32	Male	3.45
Lia	28	Female	4.6
Vin	45	Male	3.9
Katie	38	Female	2

You can think of it as an SQL table or a spreadsheet data representation.
The table represents the data of a sales team of an organization with their overall performance rating. The data is represented in rows and columns. Each column represents an attribute and each row represents a person.
The data types of the four columns are as follows −

Column	Type
Name	String
Age	Integer
Gender	String
Rating	Float

Key Points
• Heterogeneous data
• Size Mutable
• Data Mutable

A pandas DataFrame can be created using the following constructor −
pandas.DataFrame( data, index, columns, dtype, copy)

•  data
data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame.
•  index
For the row labels, the Index to be used for the resulting frame is Optional Default np.arrange(n) if no index is passed.
•  columns
For column labels, the optional default syntax is – np.arrange(n). This is only true if no index is passed.
•  dtype
The data type of each column.
•  copy
This command (or whatever it is) is used for copying of data if the default is False.

There are many methods to create DataFrames.
• Lists
• dict
• Series
• Numpy ndarrays
• Another DataFrame

Creating DataFrame from the dictionary of Series
The following method can be used to create DataFrames from a dictionary of pandas series.

print(df)

print(df.index)

print(df.columns)

Creating DataFrame from list of dictionaries
l = [{‘orange’: 32, ‘apple’: 42}, {‘banana’: 25, ‘carrot’: 44, ‘apple’: 34}]
df = pd.DataFrame(l, index=[‘test1’, ‘test2’])

print(df)

You might have noticed that we got a DataFrame with NaN values in it. This is because we didn’t the data for that particular row and column.

Creating DataFrame from Text/CSV files
Pandas tool comes in handy when you want to load data from a CSV or a text file. It has built-in functions to do this for use.

df = pd.read_csv(‘happiness.csv’)

Yes, we created a DataFrame from a CSV file. This dataset contains the outcome of the European quality of life survey. This dataset is available here. Now we have stored the DataFrame in df, we want to see what’s inside. First, we will see the size of the DataFrame.

print(df.shape)

It has 105 Rows and 4 Columns. Instead of printing out all the data, we will see the first 10 rows.
df.head(10)

There are many more methods to create a DataFrames. But now we will see the basic operation on DataFrames.

Operations on DataFrame
We’ll recall the DataFrame we made earlier.

print(df)

Now we want to create a new row column from current columns. Let’s see how it is done.
df[‘column3’] = (2 * df[‘column1’] + 3 * df[‘column2’])/5

We have created a new column column3 from column1 and  column2. We’ll create one more using boolean.
df[‘flag’] = df[‘column1’] > 99.5

We can also remove columns.
column3 = df.pop(‘column3’)

print(column3)

print(df)

Descriptive Statistics using pandas
It’s very easy to view descriptive statistics of a dataset using pandas. We are gonna use, Biomass data collected from this source. Let’s load the data first.

url = ‘https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/DAAG/biomass.csv’
df = pd.read_csv(url)
df.head()

We are not interested in the unnamed column. So, let’s delete that first. Then we’ll see the statistics with one line of code.

It’s simple as that. We can see all the statistics. Count, mean, standard deviation and other statistics. Now we are gonna find some other metrics which are not available in the describe() summary.

Mean :
print(df.mean())

Min and Max
print(df.min())

print(df.max())

Pairwise Correlation
df.corr()

Data Cleaning
We need to clean our data. Our data might contain missing values, NaN values, outliers, etc. We may need to remove or replace that data. Otherwise, our data might make any sense.
We can find null values using the following method.

print(df.isnull().any())

We have to remove these null values. This can be done by the method shown below.

newdf = df.dropna()

print(newdf.shape)

print(newdf.shape)

Pandas .Panel()
A panel is a 3D container of data. The term Panel data is derived from econometrics and is partially responsible for the name pandas − pan(el)-da(ta)-s.
The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data. They are −
• items − axis 0, each item corresponds to a DataFrame contained inside.
• major_axis − axis 1, it is the index (rows) of each of the DataFrames.
• minor_axis − axis 2, it is the columns of each of the DataFrames.

A Panel can be created using the following constructor −
The parameters of the constructor are as follows −
• data – Data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame
• items – axis=0
• major_axis – axis=1
• minor_axis – axis=2
• dtype – the Data type of each column
• copy – Copy data. Default, false

A Panel can be created using multiple ways like −
• From ndarrays
• From dict of DataFrames
• From 3D ndarray

print(p)

Note − Observe the dimensions of the empty panel and the above panel, all the objects are different.

From dict of DataFrame Objects

print(p)

Selecting the Data from Panel
Select the data from the panel using −
• Items
• Major_axis
• Minor_axis

Using Items

print p[‘Item1’]

We have two items, and we retrieved item1. The result is a DataFrame with 4 rows and 3 columns, which are the Major_axis and Minor_axis dimensions.

Using major_axis
Data can be accessed using the method panel.major_axis(index).

Using minor_axis
Data can be accessed using the method panel.minor_axis(index).

print(p.minor_xs(1))

 

Slack is a great platform for team collaboration not just that it also has one of the best API interfaces to build Chatbots.

In this post, I will walk you through building a minimal Slack Bot with Django backend. The idea is to set up a Slack Bot that will notify event when greeted through a backend.

Before we start let us understand the Slack bots life cycle:

• If you are new to Slack, It’s a messaging platform focused on team collaboration. Slack lets you create custom applications, including bots (sort of Messaging Platform as a Service). You will run the application back end to process business logic in your own server.
• To start with, you need to be part of the Slack team and have admin privilege to create a new Slack App. If you are not part of a Slack team you may create one.
• GIve the name of your company or team.

• Enter Channel Name.
• Click on See your channel in slack
• We will create a Slack App for the Slack team then we will add a Bot User to the app.
• We will create a Django based backend web application to post the messages into slack.
• After setting up the Slack App and have the backend ready to notified events.

Create a Slack App

Start by creating a Slack app here, click Create App. Then proceed with app creation, give it a name and select the Slack team.

Then you will be taken to App configuration where you need do following to get our Bot up and running.

1. Create a Bot User
2. Install Slack App to your Team

Create a BOT User

On the left pane click on Bot User then choose a user name for the Bot and set “Always Show My Bot as Online” slider to on. Click on Add Bot User to create our shipment bot.

Install Slack App to Team

Now on the left pane click Install App and install the app to your Slack team.

Once installed you will get Bot User OAuth Access Token, note down this token we will need it later while configuring Django app. This token is the passphrase for our Bot to interact with the Slack Team.

Slack Client Credentials

Also, note down the App Credentials from Basic Information on the left pane. These credentials let us talk to Slack API, so every time we send a message to Slack we should send our Client ID(CLIENT_ID) & Client Secret(CLIENT_SECRET) to identify ourselves to Slack. Similarly, we can verify if an incoming message is sent by Slack checking if the Verification Token (VERIFICATION_TOKEN) in the message is the same as the one in App Credentials.

Now we should have four key values with us.

1. Client ID — SLACK_CLIENT_ID/li>
2. Client Secret — SLACK_CLIENT_SECRET
3. Verification Token — SLACK_VERIFICATION_TOKEN
4. Bot User Token — SLACK_BOT_USER_TOKEN

Environment Setup

Let us create a new virtual environment “venv” for our project with python version 3.6.x and activate the virtual environment.

You need to activate the virtual environment before installation of other dependencies.

Now let’s install required packages

Create a Django Application

Configure Django Settings

we need to add our own application shipment as a dependency. Add the line mentioned below in the file slack/settings.py

# slack/settings.py

Then add following configurations in slack_bot/settings.py with your authentication keys from Slack.

# slack/settings.py

Now start the Django development server

Once the server is started it will print something similar to this

Ignore the migration warnings and open the URL in your browser.

Create an API endpoint

Now that we have our app server up and running we need to create an endpoint for Slack to send event messages. We will create an API view with Django as follows:

Shipment/view.py

Configure Django Routes

If you are new to web applications, routing is the way to tell web server which functions to invoke when an URL is hit with a request. When the URL is hit with a request message the corresponding function will be invoked, passing the requester message as a parameter to the function.
Add following lines in shipment/urls.py to tie shipment API class to http://localhost:8000/shipment/

slackapi.py

Functions written in slackapi.py are used to post notification/messages to slack.