[Python知识库]stage2_4

import pandas as pd
t1 = pd.Series([1,2,31,12,45,3])

print(t1)
print(type(t1))

t2 = pd.Series([12,13,14,15,16],index=list("abcde"))
print(t2)

tem_dict= {"name":"zhangsan","age":18,"tel":10086}
t3 = pd.Series(tem_dict)
print(t3)
print(t2.dtype)
print(t3.dtype)
a= t2.astype(float)
print(a.dtype)
print(t3["age"])
print(t3[1])
print(t3[:3])
print(t3[[1,2]])
print(t3[["name","age"]])
print(t3.index)
for i in t3.index:
    print(i)
print(t3.values)

import pandas as pd
import numpy as np

#读取CSV文件中的数据
df = pd.read_csv("D:\PycharmProjects\MyDemo\machine_learning\dogNames2.csv")
#print(df)

#d1 = {"name":["xiaohong","xiaoming"],"age":["18","20"],"tel":["10086","10010"]}
#t1 = pd.DataFrame(d1)
#print(df.head())
#print(df.info())
#DataFrame中排序方法
#df = df.sort_values(by="Count_AnimalName",ascending=False)
#rint(df.head(10))
#pandas取行取列的方法
#1，方括号[]写数字，表示取行，对行进行操作
#2，写字符串，表示取列，对列进行操作
#print(df[:20])
#print(df["Row_Labels"])
#t1 = pd.DataFrame(np.arange(12).reshape(3,4))
#print(t1)
#t2 = pd.DataFrame(np.arange(12).reshape(3,4),index=list("abc"),columns=list("wxyz"))
#print(t2)

#bool索引,&表示且，丨表示或
print(df[(800<df["Count_AnimalName"])&(df["Count_AnimalName"]<1000)])

import pandas as pd
import numpy as np

t1 = pd.DataFrame(np.arange(12).reshape(3,4),index=list("abc"),columns=list("wxyz"))
print(t1)
#loc取值方式
print(t1.loc["a","z"])
print(t1.loc["a"])
print(t1.loc["a",:])
print(t1.loc[:,"y"])
print(t1.loc[["a","c"],])
print(t1.loc[["a","c"],:])
print(t1.loc[:,["w","z"]])
print(t1.loc[["a","c"],["w","z"]])
#iloc取值方式
print(t1.iloc[0])
print(t1.iloc[:,[1,2]])
print(t1.iloc[[0,1],[2,3]])
print(t1.iloc[1:,:2])
t1.iloc[1:,:2]= np.nan
print(t1)
print(pd.isnull(t1))    #t1中是nan的数值
print(pd.notnull(t1))   #t1中不是nan的值
print(t1[pd.notnull(t1["w"])])  #取t1的W列中不是nan的行
print(t1.dropna(axis=0))     #删除有nan的行
print(t1.dropna(axis=0,how="any"))   #“any”表示只要有nan的行都删除
print(t1.dropna(axis=0,how="all"))    #“all”表示只有整行全部为nan才删除
#print(t1)
#print(t1.dropna(axis=0,how="any",inplace=True))    #inplace意思为原地操作，即对t1原变量进行修改
#print(t1)
print(t1.fillna(t1.mean()))    #fillna的意思填充nan的值，.mean的意思是均数
print(t1.mean())
print(t1["x"].fillna(t1["x"].mean()))  #对单独的一列的nan进行赋值操作，t1["x"]为选中x列，t1["x"].mean()使用x列的均值进行填充

import pandas as pd
from matplotlib import pyplot as plt
file_path = "IMDB-Movie-Data.csv"
df= pd.read_csv(file_path)
#print(df.info())
#print(df.head(1))
#print(df["Rating"].mean())
#获取导演人数
#print(len(set(df["Director"].tolist())))     #tolist转化为列表，set创建无序不重复的元素集，len计算长度
#print(len(df["Director"].unique()))   #unique为独一无二，自动生成捕虫回复列表
#获取演员人数
#tem_actors_list = df["Actors"].str.split(", ").tolist()
#actors_list = [i for j in tem_actors_list for i in j]#2个嵌套循环可以把嵌套列表展开为1个列表
#actors_num = len(set(actors_list))
#print(actors_num)

#选择图形，直方图，准备数据

runtime_data = df["Runtime (Minutes)"].values
max_runtime = runtime_data.max()
min_runtime = runtime_data.min()
#计算组数
print(max_runtime-min_runtime)
num_bin = (max_runtime-min_runtime)//5

plt.figure(figsize=(20,8),dpi=80)
plt.hist(runtime_data,num_bin)
plt.xticks(range(min_runtime,max_runtime+5,5))


plt.grid()
plt.show()