이 튜토리얼에서는 pandas 패키지를 사용하여 모바일 데이터 속도를 분석할 것입니다. TRAI에서 모바일 속도를 다운로드하세요. 공식 웹 사이트. 파일을 다운로드하는 단계입니다.
알고리즘
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.
CSV의 열을 살펴보겠습니다. 파일.
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네트워크 이름
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네트워크 기술
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테스트 유형
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속도
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신호 강도
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상태
pandas, numpy, matplotlib가 필요합니다. 도서관. 데이터 분석을 위한 코딩을 시작해 보겠습니다.
예
# 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)
출력
위의 프로그램을 실행하면 다음과 같은 결과를 얻을 수 있습니다.
['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']
계속...
# 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}') 출력
위의 코드를 실행하면 다음과 같은 결과를 얻을 수 있습니다.
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
계속...
# 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() 출력
위의 그래프를 실행하면 다음과 같은 그래프가 나옵니다.
결론
필요에 따라 다양한 그래프를 그릴 수 있습니다. 다양한 그래프를 그려서 데이터세트를 가지고 놀아보세요. 튜토리얼에 대해 궁금한 점이 있으면 댓글 섹션에 언급하세요.