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‫So Pandas is a software library written for the Python programming language for data manipulation

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‫and analysis.

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‫This is specifically for data and information and analysis.

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‫First, we need to import pandas.

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‫So we'll write import.

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‫pandas as pd.

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‫If you are using anaconda, Anaconda have automatically install pandas into your system

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‫so you don't need to install it separately, you just have to import it into your workspace.

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‫for pandas

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‫We will be using our customer data.

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‫csv file

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‫You can find this file in the resources section of this video.

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‫So go on, download this file and put this in your folder.

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‫We will start by importing a customer.csv file. So we will write data one

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‫This is our variable.

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‫Then we'll will write a pandas function to import

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‫csv

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‫That is pd.read_csv

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‫Then we provide the location of our csv file

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‫Remember to change

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‫This backspaces into forward slashes.

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‫Then the file name customer.csv

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‫And then headers = 0

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‫Since our first row contain the header.

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‫If we run this, we'll get our table in variable data 1

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‫If we write data 1.head, we'll get the first five rows of our data.

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‫You can see we have customer id, customer name, segment, age, country, city, state, postal code and

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‫the region as our columns.

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‫Then we have multiple customer details as rows

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‫First is a customer

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‫id

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‫This is a unique id for each customer.

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‫Second column is a customer name.

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‫Here we have a customer name, full name.

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‫Then that is the segment whether the customer belong

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‫to a consumer segment or corporate segment.

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‫Then we have a column for age. The age of customer.

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‫Then the country, city, state, postal code, region of that customer.

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‫If you want to grab more rows, you can provide the number.

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‫in this bracket, by default it is five.

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‫If you write 10 in the output you will get 10 rows

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‫Now, here you are seeing this zero one, two, three, four.

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‫This all are the index of this table.

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‫For example, 0th rows will be this row.

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‫If you want to add our customer id as an index, since our customer id is primary key.

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‫We can add it as an index.

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‫We'll write this csv file into another data type

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‫That is data 2. We'll copy

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‫the above command

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‫two and we will write another

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‫parameter that its index underscore column

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‫We are providing the location of index column,and for our data it is customer id.

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‫Which is the 0th

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‫column of our data.

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‫Since this is the first column, the index is zero.

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‫That's why we are providing zero.

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‫Similar to what we provided for headers

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‫So our headers was present in the first row. So I added 0, the location of it.

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‫here also our index column is zero.

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‫If we run this and again, if we run the head of this.

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‫You can see now zero one, two, three, four indexes out of one.

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‫Now these are our indexes.

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‫These are important and we will discuss about it in a short while.

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‫Now head command is used to view the sample of your data.

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‫If you want to know statistics of your data, you write data1

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‫Dot describe, dot describe it as a keyword.

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‫And run this.

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‫So there are only two integer values in our data.

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‫That's why we are only getting two columns here

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‫First is Region

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‫And second is post code.

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‫Here you can see the total count of value, the mean of age , the standard deviation of age, minimum

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‫age, maximum age.

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‫These are the percentile value.

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‫This is a 25 percentile value.

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‫So if you arrange all the age in ascending order, the value presented that 25th percentile of that data

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‫is this value.

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‫Similarly, this is the 50th percentile, also known as the median value. This is the 70 percentile value of

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‫age.

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‫And this is the maximum value of age.

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‫This we will discuss in univariate analysis, which we will be covering in the later part of this course.

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‫There are two ways to Index a data frame.

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‫So we discuss earlier while importing this data.

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‫We can provide index column, for our data 1index

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‫We did not provide any index column for data two our index column

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‫is customer id

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‫So if we want to view the first row of our data, we have to either use loc

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‫Or I loc, if you if we use data one.

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‫dot iloc

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‫And then we provide zero.

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‫What iloc will do is, It will grab the data that is present at the 0th Index of our data frame.

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‫So our output is same as what the first row is of our data frame.

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‫If we want to use the index column, which we defined while creating our data frame, we have to use loc

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‫And in the bracket, if we write the customer id.

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‫CG-12520

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‫In data 2 we defined our index column as customer id

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‫So now we can use loc keyword to get the data of this

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‫customer id, If we run this,

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‫You can see we are getting all the details of our customer except the customer id. since this

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‫is the index column. Similarly.

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‫If I don't know this id and I just wanted to grab the first customer

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‫I can use.

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‫iloc

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‫here also

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‫I'm getting the same detail, here

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‫I was using iloc, with iloc you have to use the serial number zero and so on with loc

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‫You can use the index column that you provided earlier

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‫So if you know the position you can use, I lock.

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‫And if you know the value, you can use the loc.

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‫Just like in this 10 data frame, you can also mention multiple values using colon operator.

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‫So for example, if I write data 2.iloc

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‫0 colon five

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‫This will give me the data of first five rows.

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‫where, the index, value is zero, one, two, three and four, the number five is excluded from

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‫this result.

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‫So I'm getting data of this five customer.

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‫You can use steps as well, if I write 2

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‫And run this.

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‫I'm getting only three results since I'm using steps, that's all for panda.

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‫We will be using panda a lot more while doing our work and will discuss new topics than in their order.