In this tutorial, we are going to analyze the mobile data speeds using the pandas package. Download the mobile speeds from the TRAI official website. Steps to download the file.
Algorithm
1. Go to the [TRAI](https://myspeed.trai.gov.in/ ) website. 2. Scroll down to the end of the page. 3. You will find mobile speed data for different months. 4. Download the September mobile data speeds.
Let's see the columns in the CSV file.
Network Name
Network Technology
Type Of Test
Speed
Signal Strength
State
We need pandas, numpy, matplotlib libraries. Let's start the coding to analyze the data.
Example
# importing requires libraries import pandas as pd import numpy as np import matplotlib.pyplot as plot # constants DATASET = 'sept19_publish.csv' NETWORK_NAME = 'JIO' STATE = 'Andhra Pradesh' # lists to store the values download_speeds = [] upload_speeds = [] states = [] operators = [] # importing the dataset using pandas data_frame = pd.read_csv(DATASET) # assigning column names for easy access data_frame.columns = ['Network', 'Technology', 'Type Of Test', 'Speed', 'Signal Str ength', 'State'] # getting unique states and operators from the dataset unique_states = data_frame['State'].unique() unique_operators = data_frame['Network'].unique() print(unique_states) print() print(unique_operators)
Output
If you run the above program you will get the following result.
['Kolkata' 'Punjab' 'Delhi' 'UP West' 'Haryana' nan 'West Bengal' 'Tamil Nadu' 'Kerala' 'Rajasthan' 'Gujarat' 'Maharashtra' 'Chennai' 'Madhya Pradesh' 'UP East' 'Karnataka' 'Orissa' 'Andhra Pradesh' 'Bihar' 'Mumbai' 'North East' 'Himachal Pradesh' 'Assam' 'Jammu & Kashmir'] ['JIO' 'AIRTEL' 'VODAFONE' 'IDEA' 'CELLONE' 'DOLPHIN']
Continuation...
# getting the data related to one network that we want
# we already declared the network previously
# this filtering the data
JIO = data_frame[data_frame['Network'] == NETWORK_NAME]
# iterating through the all states
for state in unique_states:
# getting all the data of current state
current_state = JIO[JIO['State'] == state]
# getting download speed from the current_state
download_speed = current_state[current_state['Type Of Test'] == 'download']
# calculating download_speed average
download_speed_avg = download_speed['Speed'].mean()
# getting upload speed from the current_state
upload_speed = current_state[current_state['Type Of Test'] == 'upload']
# calculating upload_speed average
upload_speed_avg = upload_speed['Speed'].mean()
# checking if the averages or nan or not
if pd.isnull(download_speed_avg) or pd.isnull(upload_speed_avg):
# assigning zeroes to the both speeds
download_speed, upload_speed = 0, 0
else:
# appending state if the values are not nan to plot
states.append(state)
download_speeds.append(download_speed_avg)
upload_speeds.append(upload_speed_avg)
# printing the download ans upload averages
print(f'{state}: Download Avg. {download_speed_avg:.3f} Upload Avg. {upload _speed_avg:.3f}')Output
If you run the above code you will get the following result.
Kolkata: Download Avg. 31179.157 Upload Avg. 5597.086 Punjab: Download Avg. 29289.594 Upload Avg. 5848.015 Delhi: Download Avg. 28956.174 Upload Avg. 5340.927 UP West: Download Avg. 21666.673 Upload Avg. 4118.200 Haryana: Download Avg. 6226.855 Upload Avg. 2372.987 West Bengal: Download Avg. 20457.976 Upload Avg. 4219.467 Tamil Nadu: Download Avg. 24029.364 Upload Avg. 4269.765 Kerala: Download Avg. 10735.611 Upload Avg. 2088.881 Rajasthan: Download Avg. 26718.066 Upload Avg. 5800.989 Gujarat: Download Avg. 16483.987 Upload Avg. 3414.485 Maharashtra: Download Avg. 20615.311 Upload Avg. 4033.843 Chennai: Download Avg. 6244.756 Upload Avg. 2271.318 Madhya Pradesh: Download Avg. 15757.381 Upload Avg. 3859.596 UP East: Download Avg. 28827.914 Upload Avg. 5363.082 Karnataka: Download Avg. 10257.426 Upload Avg. 2584.806 Orissa: Download Avg. 32820.872 Upload Avg. 5258.215 Andhra Pradesh: Download Avg. 8260.547 Upload Avg. 2390.845 Bihar: Download Avg. 9657.874 Upload Avg. 3197.166 Mumbai: Download Avg. 9984.954 Upload Avg. 3484.052 North East: Download Avg. 4472.731 Upload Avg. 2356.284 Himachal Pradesh: Download Avg. 6985.774 Upload Avg. 3970.431 Assam: Download Avg. 4343.987 Upload Avg. 2237.143 Jammu & Kashmir: Download Avg. 1665.425 Upload Avg. 802.925
Continuation...
# plotting the graph'
fix, axes = plot.subplots()
# setting bar width
bar_width = 0.25
# rearranging the positions of states
re_states = np.arange(len(states))
# setting the width and height
plot.figure(num = None, figsize = (12, 5))
# plotting the download spped
plot.bar(re_states, download_speeds, bar_width, color = 'g', label = 'Avg. Download
Speed')
# plotting the upload speed
plot.bar(re_states + bar_width, upload_speeds, bar_width, color='b', label='Avg. Up
load Speed')
# title of the graph
plot.title('Avg. Download|Upload Speed for ' + NETWORK_NAME)
# x-axis label
plot.xlabel('States')
# y-axis label
plot.ylabel('Average Speeds in Kbps')
# the label below each of the bars,
# corresponding to the states
plot.xticks(re_states + bar_width, states, rotation = 90)
# draw the legend
plot.legend()
# make the graph layout tight
plot.tight_layout()
# show the graph
plot.show()Output
If you run the above graph you will get the following graph.
Conclusion
You can plot different graphs based on your needs. Play with the dataset by plotting different graphs. If you have any doubts regarding the tutorial, mention them in the comment section.