Python——数据分析

Dataframe:数据帧
Dataframe VS Excel：一个DataFrame相当于一个Excel的sheet

#创建一个空Excel
import panda as pd
df = pd.DataFrame()
df.to_excel("D:/2019.上/3月/output.xlsx")

#在空Excel的表中填充数据
import panda as pd
df = pd.DataFrame({'ID' : [1,2,3], 'Name' : ['KX’ , 'CM'  , 'LY']})
df.to_excel("D:/2019.上/3月/output.xlsx")

#设置ID为索引
import panda as pd
df = pd.DataFrame({'ID' : [1,2,3], 'Name' : ['KX’ , 'CM'  , 'LY']})
df = df.set_index('ID')
df.to_excel("D:/2019.上/3月/output.xlsx")

import pandas as pd

#csv文件，多了一行,第一行是乱码，或者有一个脏数据
data = pd.read_csv('D:/大四上/毕业论文/deepin/url.csv', head=1)
#csv文件，第一行没有列名
data = pd.read_csv('D:/大四上/毕业论文/deepin/url.csv', head=None)
data.columns = ["url", "detail"]
data.set_index("url", inplace=True)
print(data.columns)
#正常的csv文件设置索引
data = pd.read_csv('D:/大四上/毕业论文/deepin/url.csv', index = "ID")

print(data.head())  #默认前五行 ,可设置
print(data.tail())
print(data.shape)   #输出几行几列
print(data.columns)  #输出列名

import pandas as pd

s1 = pd.Series([1, 2, 3], index=[1, 2, 3], name = "A")
s2 = pd.Series([10, 20, 30], index=[1, 2, 3], name = "B")
s3 = pd.Series([100, 200, 300], index=[1, 2, 3], name = "C")
df = pd.DataFrame([s1,s2,s3])
print(df)

运行结果：

import pandas as pd

s1 = pd.Series([1, 2, 3], index=[1, 2, 3], name = "A")
s2 = pd.Series([10, 20, 30], index=[1, 2, 3], name = "B")
s3 = pd.Series([100, 200, 300], index=[1, 2, 3], name = "C")
df = pd.DataFrame({s1.name:s1,s2.name:s2,s3.name:s3})
print(df)

运行结果：

import pandas as pd

s1 = pd.Series([1, 2, 3])
print(s1)
s2 = {"A":1, "B":2, "C":3}
print(s2.keys())
print(s2.values())

import pandas as pd

s1 = pd.Series([1, 2, 3])
print(s1.index[0])
print(s1.values[0])
s2 = {"A":1, "B":2, "C":3}
print(s2.keys())
print(s2.values())
s3 = pd.Series(s2)
print(s3)

运行结果：

data["ID"][2] = data["ID"].at[2]

books= pd.read_excel("C:/Users/Administrator/Desktop/books.xlsx",index_col="ID")
#books["总价"] = books["单价"]*books["数量"]   #使用于列运算

for i  in books.index:  #(使用于对特定单元格进行计算)
    books["总价"].at[i] = books["单价"].at[i]*books["数量"].at[i]
 
print(books)

def add2(x):
    return x+2

print(books)
#books["单价"] = books["单价"]+2
#等用于books["单价"] = books["单价"].apply(add2)
#等同于books["单价"] = books["单价"].apply(lambda x :  x+2)

import pandas as pd

books= pd.read_excel("C:/Users/Administrator/Desktop/books.xlsx",index_col="ID")

books.sort_values(by=["值得于否","单价" ],inplace=True ,ascending = [True,False])
#inplace = True在当前books上进行排序，不生成新的.默认是从小到大排序=ascend = True
print(books)

#Excel筛选

import pandas as pd

books= pd.read_excel("C:/Users/Administrator/Desktop/books.xlsx",index_col="ID")
print(books)

def age_10_to_30(a):
    return 10<=a<=30
def level_a(s):
    return 75<=s<=100

books = books.loc[books['Age'].apply(age_10_to_30)].loc[books["Score"].apply(level_a)]
print(books)

相当于

books = books.loc[books['Age'].apply(lambda x :10<=x<30)]. \
        loc[books["Score"].apply(lambda y:70<=y<100)]

#制作柱图

import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号

books= pd.read_excel("C:/Users/Administrator/Desktop/books.xlsx",index_col="ID")
print(books)
books = books.sort_values(by = "Score", ascending = False)
#books.plot.bar(x="Age", y="Score",color="orange",title="成绩与分数的关系")

plt.bar(books.Age, books.Score, color ="orange")
plt.xticks(books.Score, rotation = 90)
plt.xlabel("Score")
plt.ylabel("Age")
plt.title("年龄与成绩", fontsize =16)
plt.tight_layout()  #紧凑型布局
plt.show()

制分组柱形图

import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号

books= pd.read_excel("C:/Users/Administrator/Desktop/books.xlsx",index_col="ID")
print(books)

books.sort_values(by="一", inplace=True, ascending =False )
books.plot.bar(x="Student", y=["一","二"],color=["orange","black"])
plt.title("学生与成绩", fontsize =16,fontweight="bold")
plt.xlabel("学生", fontweight="bold")
plt.ylabel("成绩", fontweight="bold")

ax = plt.gca()  #获取轴
ax.set_xticklabels(books["Student"],rotation=45, ha="right")
f = plt.gcf()#调上下大小间距
f.subplots_adjust(left =0.2, bottom =0.3)

#plt.tight_layout()
plt.show()