Python中的探索性数据分析 |设置 1
探索性数据分析是一种用视觉技术和所有统计结果分析数据的技术。在应用任何机器学习模型之前，我们将学习如何应用这些技术。
要获取所使用的csv文件的链接，请单击此处。
加载库：
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import trim_mean
加载数据中：
data = pd.read_csv("state.csv")
# Check the type of data
print ("Type : ", type(data), "\n\n")
# Printing Top 10 Records
print ("Head -- \n", data.head(10))
# Printing last 10 Records
print ("\n\n Tail -- \n", data.tail(10))
输出 ：
Type : class 'pandas.core.frame.DataFrame'
Head --
State Population Murder.Rate Abbreviation
0 Alabama 4779736 5.7 AL
1 Alaska 710231 5.6 AK
2 Arizona 6392017 4.7 AZ
3 Arkansas 2915918 5.6 AR
4 California 37253956 4.4 CA
5 Colorado 5029196 2.8 CO
6 Connecticut 3574097 2.4 CT
7 Delaware 897934 5.8 DE
8 Florida 18801310 5.8 FL
9 Georgia 9687653 5.7 GA
Tail --
State Population Murder.Rate Abbreviation
40 South Dakota 814180 2.3 SD
41 Tennessee 6346105 5.7 TN
42 Texas 25145561 4.4 TX
43 Utah 2763885 2.3 UT
44 Vermont 625741 1.6 VT
45 Virginia 8001024 4.1 VA
46 Washington 6724540 2.5 WA
47 West Virginia 1852994 4.0 WV
48 Wisconsin 5686986 2.9 WI
49 Wyoming 563626 2.7 WY
代码 #1：向数据框添加列
# Adding a new column with derived data
data['PopulationInMillions'] = data['Population']/1000000
# Changed data
print (data.head(5))
输出 ：
State Population Murder.Rate Abbreviation PopulationInMillions
0 Alabama 4779736 5.7 AL 4.779736
1 Alaska 710231 5.6 AK 0.710231
2 Arizona 6392017 4.7 AZ 6.392017
3 Arkansas 2915918 5.6 AR 2.915918
4 California 37253956 4.4 CA 37.253956
代码#2：数据描述
data.describe()
输出 ： 
代码#3：数据信息
data.info()
输出 ：
RangeIndex: 50 entries, 0 to 49
Data columns (total 4 columns):
State 50 non-null object
Population 50 non-null int64
Murder.Rate 50 non-null float64
Abbreviation 50 non-null object
dtypes: float64(1), int64(1), object(2)
memory usage: 1.6+ KB
代码 #4：重命名列标题
# Rename column heading as it
# has '.' in it which will create
# problems when dealing functions
data.rename(columns ={'Murder.Rate': 'MurderRate'}, inplace = True)
# Lets check the column headings
list(data)
输出 ：
['State', 'Population', 'MurderRate', 'Abbreviation']
代码 #5：计算平均值
Population_mean = data.Population.mean()
print ("Population Mean : ", Population_mean)
MurderRate_mean = data.MurderRate.mean()
print ("\nMurderRate Mean : ", MurderRate_mean)
输出：
Population Mean : 6162876.3
MurderRate Mean : 4.066
代码 #6：修剪后的平均值
# Mean after discarding top and
# bottom 10 % values eliminating outliers
population_TM = trim_mean(data.Population, 0.1)
print ("Population trimmed mean: ", population_TM)
murder_TM = trim_mean(data.MurderRate, 0.1)
print ("\nMurderRate trimmed mean: ", murder_TM)
输出 ：
Population trimmed mean: 4783697.125
MurderRate trimmed mean: 3.9450000000000003
代码 #7：加权平均值
# here murder rate is weighed as per
# the state population
murderRate_WM = np.average(data.MurderRate, weights = data.Population)
print ("Weighted MurderRate Mean: ", murderRate_WM)
输出 ：
Weighted MurderRate Mean: 4.445833981123393
代码#8：中位数
Population_median = data.Population.median()
print ("Population median : ", Population_median)
MurderRate_median = data.MurderRate.median()
print ("\nMurderRate median : ", MurderRate_median)
输出 ：
Population median : 4436369.5
MurderRate median : 4.0