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法律使用 Python 的 Pandas 分析 TRAI 的移動資料速度
在本教程中，我們將利用 pandas 包分析移動資料速度。從TRAI 官方網站下載移動速度。下載檔案步驟。
演算法
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.
我們來看看CSV 檔案中的列。
網路名稱
網路技術
測試型別
速度
訊號強度
狀態
我們需要pandas、numpy、matpoltlib 庫。讓我們開始編碼以分析資料。
示例
# 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)
輸出
如果您執行以上的程式，您將獲得以下結果。
['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']
繼續...
# 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}')輸出
如果您執行以上程式碼，您將獲得以下結果。
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
繼續...
# 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()輸出
如果您執行以上的圖表，您將獲得以下圖表。
結論
根據您的需要，您可以繪製不同的圖表。透過繪製不同的圖表來使用該資料集。如果您對本教程有任何疑問，可以在評論部分中提到。
更新時間： 2020 年 7 月 8 日
94 次瀏覽
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