lab3 file for python for 4 grade

1. Business
Analytics
Eng. Tasneem Mohammed Helil

2. Operations
1. columns, indexandvaluesproperties
2. Usefull methods:
• Unique ( )
• Nunique ( )
• value_counts( )
• sort_values(by="colna“ ,ascending=bool ,inplace = bool)

3. Exercise
1. Create a DataFrame df with 5rows and 3columns named A, B, and C,
filled with randomintegers between 1and 100.
2. Print the column names, index, and values of the DataFrame.
3. Get the unique values on column A
4. Count the number of unique values on column B
5. Count how many times each value appears on column C
6. Sort the df by column Ain ascending order

4. Operations
1. Statisticsmethods:
• mean( )  of columns or rows
• median ( )
• mode ( )
• std ( )
• coefficient of variation (CV) = Mean / Standard Deviation × 100

5. Exercise
1. Calculate the mean of columns A, B, and C
2. Calculate the median of columns A, A only
3. Calculate the mode of column A
4. Calculate the standard deviation of rows

6. Quantiles
Position = (N−1) × q
First Quartile 25% of the $records are less than or equal to $result
Median 50% of the $records are less than or equal to $result
Third Quartile 75% of the $records are less than or equal to $result
N = length of the list
q = quantile percentage

7. Quantiles exercise
calculate the 0.25 and 0.5 quantiles for this list:
[1, 2, 3, 5, 6, 6, 4, 7, 8]

8. Quartiles cont.
If the quantile position is a fractional number (e.g., 2.5), then you need to
interpolate between the two nearest values:
Interpolated Value = v + ( f × ( p – v ) )
V = value of integer part (2)
P = v + 1 (3)
F = fractional part (0.5)

9. Correlation
Correlation is a statistical measure that describes the strength and direction of a relationship
between two variable
Types of Correlation:
• Positive Correlation: As one variable increases, the other variable also increases.
• Negative Correlation: As one variable increases, the other variable decreases No.
• Correlation: There is no discernible relationship between the two variables.
Calculate the correlation between columns A and B  df['A'].corr(df['B'])

10. Operations
Calculatingfor rowsandcolumns:
1. Max
2. Min
3. Sum
4. count

11. Exercise
1. Calculate the max and min of all columns A, B, and C
2. Calculate the sum of columns A, B only
3. Calculate the count of rows

12. Operations
1. head returns the first n rows
2. tail returns the last n rows
3. Info summary of index dtype, column dtypes, non-null values, and
memory usage
4. Describe Generates descriptive statistics for numerical columns (count,
mean, standard deviation, min, max, and quantiles)

13. Thank You