Using csv module to read the data in Pandas

The so-called CSV (Comma Separated Values) format is the most common import and export format for spreadsheets and databases. There were various formats of CSV until its standardization. The lack of a well-defined standard means that subtle differences often exist in the data produced and consumed by different applications. These differences can make it annoying to process CSV files from multiple sources. For that purpose, we will use Python’s csv library to read and write tabular data in CSV format.
For link to the CSV file used in the code click here.
Code #1: We will use csv.DictReader() function to import the data file into the Python’s environment.

Python3

# importing the csv module
import csv

# Now let's read the file named 'auto-mpg.csv'
# After reading as a dictionary convert
# it to Python's list
with open('auto-mpg.csv') as csvfile:
    mpg_data = list(csv.DictReader(csvfile))

# Let's visualize the data
# We are printing only first three elements
print(mpg_data[:3])

Output : 

As we can see the data is stored as a list of ordered dictionary. Let’s perform some operations on the data for better understanding.
Code #2: 

Python3

# Let's find all the keys in the dictionary
print(mpg_data[0].keys)

# Now we would like to find out the number of
# unique values of cylinders in the car in our dataset
# We create a set containing the cylinders value
unique_cyl = set(data['cylinders'] for data in mpg_data)

# Let's print the values
print(unique_cyl)

Output : 

As we can see in the output, we have 5 unique values of cylinders in our dataset.
Code #3: Now let’s find out the value of average mpg for each value of cylinders.

Python3

# Let's create an empty list to store the values
# of average mpg for each cylinder
avg_mpg = []

# c is the current cylinder size
for c in unique_cyl:
    # for storing the sum of mpg
    mpgbycyl = 0
    # for storing the sum of cylinder
    # in each category
    cylcount = 0

    # iterate over all the data in mpg
    for x in mpg_data:
        # Check if current value matches c
        if x['cylinders']== c:
            # Add the mpg values for c
            mpgbycyl += float(x['mpg'])
            # increment the count of cylinder
            cylcount += 1

    # Find the average mpg for size c
    avg = mpgbycyl/cylcount
    # Append the average mpg to list
    avg_mpg.append((c, avg))

# Sort the list
avg_mpg.sort(key = lambda x : x[0])

# Print the list
print(avg_mpg)

Output : 

As we can see in the output, the program has successfully returned a list of tuples containing the average mpg for each unique cylinder type in our dataset.