GRADE XII - IP - CSV ASSIGNMENT

 CSV ASSIGNMENT

Write the source code in the notebook.

1. Suppose a CSV file named Employee.csv has the following content:

Empid, Name, Age, City, Salary

100, Ritesh, 25, Mumbai, 15000

101, Aakash, 26, Goa, 16000

102, Mahima, 27, Hyderabad, 20000

103, Lakshay, 23, Delhi, 18000

104, Manu, 25, Mumbai, 25000

105, Nidhi, 26, Delhi

106, Geetu, 30, Bangalore, 28000

Example 1:

# Reading the Emplyee.csv data in a dataframe

import pandas as pd

df = pd.read_csv(“E:\\Data\\Employee.csv”)

print(df)

One thing to remember is that the missing values from the CSV fill shall be treated

as NaN(Not a Number) in pandas dataframe.

We can see the total number of rows (records) and columns (fields) present in the

table with the help of shape command.

>>>df.shape

(8, 5)

2. Reading CSV file with specific columns

We can read specific columns from a CSV file using usecols attribute of read_csv()

function.

Example 2:

import pandas as pd

df = pd.read_csv(“E:\\Data\\Employee.csv”, usecols = [‘Name’, ‘Age’,‘Salary’])

print(df)

3. Reading CSV file with specific rows

You can display selective record/rows using nrows option or attribute used with

read_csv() method.

Example 3:

import pandas as pd

df = pd.read_csv(“E:\\Data\\Employee.csv”, nrows = 5)

print(df)

4. Reading CSV file without header

If you do not want to display the first row as the header for dataframe using

Employee table, then this can be done by specifying None argument for header

option or skiprows option using read_csv() method.

Example 4:

import pandas as pd

df = pd.read_csv(“E:\\Data\\Employee.csv”, nrows = 5, header = None)

print(df)

5. Reading CSV file without index

You can also read and load the record into a dataframe without displaying their

respective index number by specifying the attribute index_col = 0 using the

read_csv() method.

Example 5:

import pandas as pd

df = pd.read_csv(“E:\\Data\\Employee.csv”, nrows = 5, index_col = 0)

print(df)

6. Reading CSV file with new column names

You can read the CSV file into a dataframe with new column names using name

attribute.

Example 6:

import pandas as pd

df = pd.read_csv(“E:\\Data\\Employee.csv”, nrows = 5, skiprows = 1,

name = [‘E_id’, ‘Ename’, ‘E_age’, ‘Ecity’, ‘Esalary’])

print(df)

7. Writing a CSV file with default index

To create a CSV file from a dataframe, the to_csv() method is used.

Example 7:

# Copying the content of Employee.csv to a new CSV file

import pandas as pd

df = pd.read_csv(“E:\\Data\\Employee.csv”)

df.to_csv(“E:\\Data\\Empnew.csv”)

Upon executing the above commands, Empnew.scv file shall be created containing the same content as Employee.csv with default index values.

Example 8:

# Copying the content of Employee.csv to a new CSV file

import pandas as pd

student = {'RollNo' : [1,2,3,4,5,6],

'StudName' : ['Teena' ,'Rinku', 'Payal', 'Akshay', 'Garvit','Yogesh'],

'Marks' : [90, 78, 89, 77, 97,98],

'Class' : ['11A', '11B', '11C', '11D', '11E', '11F']}

df = pd.DataFrame(student, columns = ['RollNo', 'StudName', 'Marks','Class'])

df.to_csv("D:\\Student.csv")

9. Copying Fields into a New File

In certain situations, it is required to create a duplicate file containing only the

selected fields. For this purpose we use columns attribute of to_csv() function.

Example 9:

# Creating a duplicate csv file with selective columns

import pandas as pd

df = pd.read_csv(“E:\\Data\\Employee.csv”)

df.to_csv(“E:\\Data\\Emp.csv”, columns = [‘Empid’, ‘Name’])

The above code will create a new CSV file Emp.csv with only Empid and Name columns.

***********************************************************

#**********************Saving DataFrame as CSV *******************************

dfE=pd.DataFrame({'Empno':[100,101,102,103,104,105,106,107,108,109,110,111,112],

                                    'Name':['Sunita Sharma','Ashok Singhal',                                                    

'Sumit Avasti','Jyoti Lamba','Martin S.','Binod Goel',                                                       

'Chetan Gupta','Sudhir Rawat','Kavita Sharma',                                                  

'Tushar Tiwari','Anand Rathi','Sumit Vats','Manoj Kaushik'],

                    'Department':['RESEARCH','SALES','SALES',                                                    

'RESEARCH','SALES','SALES','ACCOUNTS','RESEARCH',                                                     

'ACCOUNTS','SALES','OPERATIONS','RESEARCH','OPERATIONS'],                                              

 

'Salary':[45600,43900,27000,45900,32500,45200,36800,                                               

37000,42900,49500,41600,47800,43600],                                               

 

'Commission':[5600,3900,7000,4900,3500,4200,6800,7000,                                                       

4900,4500,8200,np.nan,np.nan],                                                  

 

'Job':['CLERK','SALESMAN','SALESMAN','MANAGER',                                                      

'SALESMAN','MANAGER','MANAGER','ANALYST','CLERK',                                             

'MANAGER','SR_MANAGER','SR_MANAGER','CLERK']})

print(dfE)

print("*"*50)

 

dfE.to_csv("D:\GRADE XII PYTHON/mihir1.csv")

 

CSV ASSIGNMENT_2

1.# Importing and exporting data between pandas and CSV file.
# To create and open a data frame using ‘Student_result.csv’ file using Pandas.
# To display row labels, column labels data types of each  column and the dimensions
# To display the shape (number of rows and columns) of the CSV file.

Sol:
import pandas as pd
#import csv

#Reading the Data
df = pd.read_csv("student_result.csv")
# Display Name of Columns
print(df.columns)

# Display no of rows and column
print(df.shape)

# Display Column Names and their types
print(df.info())

 

2. Read the ‘Student_result.csv’ to create a data frame and do the following  operation:
# To display Adm_No, Gender and Percentage from ‘student_result.csv’ file.
# To display the first 5 and last 5 records from ‘student_result.csv’ file.

Sol:

import pandas as pd
#import csv

#To display Adm_No, Gender and Percentage from ‘student_result.csv’ file.
df = pd.read_csv("student_result.csv",usecols = ['ADM_NO','GENDER', 'PERCENTAGE'])

print("To display Adm_No, Gender and Percentage from ‘student_result.csv’ file.")
print(df)

#To display first 5 and last 5 records from ‘student_result.csv’ file.
df1 = pd.read_csv("student_result.csv")
print(df1.head())
print(df1.tail())

 

 

3.# Read the ‘Student_result.csv’ to create a data frame and do the following  operation:

# To display Student_result file with new column names.

# To modify the Percentage of student below 40 with NaN value in dataframe.

 

import pandas as pd

import numpy as np

import csv

 

df = pd.read_csv("student_result.csv")

print(df)

 

#To display Student_result file with new column names.

df1 = pd.read_csv("student_result.csv",skiprows = 1,names = ['Adno','Sex','Name','Eng','Hin',

'Maths','Sc.','SSt','San','IT','Perc'])

 

print("To display Student_result file with new column names")

print(df1)

 

# To modify the Percentage of student below 40 with NaN value.

df2 = pd.read_csv("student_result.csv")

print(df2)

 

print("To modify the Percentage of student below 40 with NaN value.")

df2.loc[(df2['PERCENTAGE'] <40, 'PERCENTAGE')] = np.nan

print(df2)

 

4. # Read the ‘Student_result.csv’ to create a data frame and do the following  operation:

# To create a duplicate file for ‘student_result.csv’ containing Adm_No, Name and Percentage.

# Write the statement in Pandas to find the highest percentage and also print the student’s name and percentage.

 

import pandas as pd

import numpy as np

import csv

 

# To create a duplicate file for ‘student_result.csv’ containing Adm_No, Name and Percentage.

df = pd.read_csv("student_result.csv")

df.to_csv('copyStudent_result.csv',columns=['ADM_NO',"STUDENT'S_NAME","PERCENTAGE"])

# Display Copied Dataframe

df2=pd.read_csv("copyStudent_result.csv")

print(df2)

 

# find the highest percentage and also print the student’s name and percentage.

df1 = pd.read_csv("student_result.csv")

df1 = df1[["STUDENT'S_NAME",'PERCENTAGE']]

[df1.PERCENTAGE== df1['PERCENTAGE'].max()]

print(df1)

 

5. # Replace all negative values in a data frame with a 0.

import pandas as pd

data = {'sales1':[10,20,-4,5,-1,15], 'sales2':[20,15,10,-1,12,-2]}

df = pd.DataFrame(data)

print("Data Frame")

print(df)

print('Display DataFrame after replacing every negative value with 0')

df[df<0]=0

print(df)

 

6. import pandas as pd

import numpy as np

Srec={'sid':[101,102,103,104,np.nan,106,107,108,109,110],

'sname':['Amit','Sumit',np.nan,'Aman','Rama','Neeta','Amjad','Ram','Ilma','Raja'],

'smarks':[98,67,np.nan,56,38,98,67,np.nan,56,np.nan],

'sgrade':[np.nan,np.nan,'A1','C1','D','A1','B2',np.nan,'B2','A2'],

'remark':['P','P','P','F',np.nan,'P','P','F','P','P'],

'mobile':[9990009991,9990009992,9990009993,np.nan,9990009995,np.nan, 9990009997,

 

9990009998, np.nan,9999010000]}     

# Convert the dictionary into DataFrame

df=pd.DataFrame(Srec)

print("\n- Dataframe Before Replacing NaN with 999-\n")

print(df)

 

#Replace missing value with zeros

print("\n-After Replacing missing value with 999-\n")

df=df.fillna(999)

print(df)

 

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