Python Data Analysis Instance operations

import pandas as pd  #导入pandasimport matplotlib.pyplot as plt #导入matplotlibfrom pylab import *mpl.rcParams[‘font.sans-serif‘] = [‘SimHei‘]mpl.rcParams[‘axes.unicode_minus‘] = False%matplotlib inline

Data reading and indexing

bra = pd.read_csv(‘data/bra.csv‘)bra.head()

Select columns

bra.content

bra[[‘creationTime‘,‘productColor‘]].head()

Select rows

bra[1:6]

Select Rows and Columns

bra.ix[[2,3],[1,3]]  #使用ix

bra.ix[1:5,[‘productColor‘]]  

bra.iloc[[2,3],[1,3]] #使用iloc

bra.loc[1:5,[‘content‘,‘creationTime‘,‘productSize‘]] #使用loc

bra.loc[1:5,‘content‘:‘userClientShow‘]

Data preprocessing missing values

bra.describe() #查看数据的分布情况,可返回变量和观测的数量、缺失值和唯一值的数目、平均值、分位数等相关信息

bra[‘userClientShow‘].unique()  #userClientShow列有几种选项

bra[‘userClientShow‘].isnull().sum() #初始缺失值数量

bra[‘userClientShow‘].fillna(‘不详‘,inplace=True) #缺失值替换为“不详”bra[‘userClientShow‘].isnull().sum() #赋值后的缺失值数量

New column

bra.dtypes #查看属性

bra[‘creationTime‘] = pd.to_datetime(bra[‘creationTime‘]) #更新类型bra.dtypes

bra[‘hour‘] = [i.hour for i in bra[‘creationTime‘]] #新建hour列bra

String manipulation

bra.productSize.unique() #查看productSize的唯一值

cup = bra.productSize.str.findall(‘[a-zA-Z]+‘).str[0] #新增列cupcup2 = cup.str.replace(‘M‘,‘B‘)cup3 = cup2.str.replace(‘L‘,‘C‘)cup4 = cup3.str.replace(‘XC‘,‘D‘)bra[‘cup‘] = cup4  bra.head()

bra[‘cup‘].unique() #查看cup唯一值

Data conversion

bra.productColor.unique() #查看productColor唯一值

def getColor(s):    if ‘黑‘ in s:        return ‘黑色‘    elif ‘肤‘ in s:        return ‘肤色‘    elif ‘蓝‘ in s:        return ‘蓝色‘    elif ‘红‘ in s:        return ‘红色‘    elif ‘紫‘ in s:        return ‘紫色‘    elif ‘白‘ in s:        return ‘白色‘    elif ‘粉‘ in s:        return ‘粉色‘    elif ‘灰‘ in s:        return ‘灰色‘    elif ‘绿‘ in s:        return ‘绿色‘    elif ‘青‘ in s:        return ‘青色‘    else:        return sbra[‘color‘] = bra[‘productColor‘].map(getColor) #从productColor列查询，赋值到定义的函数getColor，最终新增列colorbra

bra.color.unique() #查询color的唯一值

Visualization of data

x = [1991,1992,1993,1994,1995,1996,1997]y = [23,56,38,29,34,56,92]plt.plot(x,y) #调用函数plot

plt.figure(figsize=(8,6),dpi=80) #调用函数firgureplt.plot(x,y)

hour = bra.groupby(‘hour‘)[‘hour‘].count()  #hour列排序hour

plt.xlim(0,25) #横轴0~25plt.plot(hour,linestyle=‘solid‘,color=‘royalblue‘,marker=‘8‘) #颜色深蓝

cup_style = bra.groupby(‘cup‘)[‘cup‘].count() #cup列唯一值得数量cup_style

plt.figure(figsize=(8,6),dpi=80)labels = list(cup_style.index)plt.xlabel(‘cup‘) #x轴为cupplt.ylabel(‘count‘) #y轴为count数量plt.bar(range(len(labels)),cup_style,color=‘royalblue‘,alpha=0.7) #alpha为透明度plt.xticks(range(len(labels)),labels,fontsize=12)plt.grid(color=‘#95a5a6‘,linestyle=‘--‘,linewidth=1,axis=‘y‘,alpha=0.6)plt.legend([‘user-count‘])for x,y in zip(range(len(labels)),cup_style):plt.text(x,y,y,ha=‘center‘,va=‘bottom‘)

color_style = bra.groupby(‘color‘)[‘color‘].count() #color列唯一值得数量color_style

plt.figure(figsize=(8,6),dpi=80)plt.subplot(facecolor=‘gainsboro‘,alpha=0.2)colors = [‘brown‘,‘orange‘,‘gray‘,‘white‘,‘pink‘,‘purple‘,‘red‘,‘green‘,‘wheat‘,‘blue‘,‘gold‘,‘springgreen‘,‘black‘] #颜色种类labels = list(color_style.index)plt.xlabel(‘count‘) #x轴为count数量plt.ylabel(‘color‘) #y轴为colorplt.title(‘Color Distribution‘) #定义标题plt.barh(range(len(labels)),color_style,color=colors,alpha=1)plt.yticks(range(len(labels)),labels,fontsize=12)plt.grid(color=‘#95a5a6‘,linestyle=‘--‘,linewidth=1,axis=‘x‘,alpha=0.4)

bra.head(30)

Python Data Analysis Instance operations