# Data Analytics Tutorial for Beginners

## Basic Concept of Data Analytics : Part-2

1. How to get the first row value :
``````#Python counts from 0

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("C:\\Chegg python\\100 Sales Records.csv")

print(df.loc)
``````

Output :

2. How to get the 100th row value :

``````#Python counts from 0 that's why we write 99 instead of 100

print(df.loc)``````

3. How to get the last row :

``print(df.tail(n=1))``

Output :

### Sub-setting multiple rows

4. Select the First, 10th, and 100th rows

``print(df.loc[[0,9,99]])``

Output :

5. How to get 100th row through iloc :

``print(df.iloc)``

Output :

6. Using -1 through iloc get the last row :

``print(df.iloc[-1])``

Output :

With iloc, we can pass in the -1 to get last row – something we couldn’t do with loc.

7. Select the First, 10th, and 100th rows

``print(df.iloc[[0,9,99]])``

Output :

### Sub-setting columns

• The Python slicing syntax use a colon, :
• If we have just a colon, the attribute refers to everything.
• So, if we just want to get the first column using the loc or iloc syntax, we can write something
• like df. loc[: , [columns]] to subset the columns(s)

8. Select column with loc

``````#note the position of the colon :
#it is used to select all rows

subset = df.loc[:,['Country',  'Sales Channel']]

Output :

9. Subset columns with iloc

``````#iloc will allows us to use integers
# -1 will select last column

subset = df.iloc[:,[2,4,-1]]

Output :

### Sub-setting Columns by Range

10. Create a range of integers from 0 to 5 inclusive

``````small_range = list(range(5))
print(small_range)``````

Output :

[0, 1, 2, 3, 4]

### Sub-setting Rows and Columns

11. Using loc

``print(df.loc[42,'Country'])``

Output :

The Gambia

12. Using iloc

``````#42th row and 1st column value

print(df.iloc[42,1])``````

Output :

The Gambia

### Sub-setting Rows and Columns

13. Get the First, 10th, and 100th rows

from the 1st, 4th, and 6th column

``print(df.iloc[[0,9,99], [0,3,5]])``

Output :

14. If we use the column names directly, it makes code a bit easier to read

``````#note now we have to use loc, instead of iloc

print(df.loc[[0,9,99], ['Region','Sales Channel','Order Date']])``````

Output :

### Grouped Means

• For each Order ID in our data, what was the average Unit Cost?
• We split our data into parts by order ID
• Then we get the “Unit Cost” column and calculate the mean
``````
#print first five row of given column with group by and mean function

Output :

### Grouped Frequency Counts

• Use the nunique to get counts of unique values on a Pandas Series
``````

Output :

### Basic Plot

``````Country_total_profit =df.head(n=10).groupby('Country')['Total Profit'].mean()
print(Country_total_profit)

Country_total_profit.plot()``````

Output :

### Visual Representation of data

• Histogram
• Frequency Polygon
• Ogive
• Pie-chart
• Steam & leaf plot
• Pareto chart
• Scatter plot

For Visual Representation of dataVisit here

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