版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/jiaoyangdetian/article/details/81056410
import pandas as pd
import numpy as np
def pandasWork1(): # DataFrame 初始化,与数据的获取
one = np.array(['name0', 'name1', 'name2', 'name3', 'name4', 'name5'])
two = list([[1, 1, 1], [1, 0, 1], [0, 0, 0], [1, 0, 1], [0, 1, 0], [1, 1, 0]])
three = list([[2, 4, 6], [0, 5, 9], [8, 3, 2], [4, 5, 9], [1, 2, 9], [2, 8, 0]])
four = list([[0.1, 0.2, 0.3], [0.1, 0.2, 0.7], [0.1, 0.4, 0.3], [0.1, 0.9, 0.3], [0.2, 0.2, 0.3], [0.1, 0.5, 0.3]])
dataFrame = pd.DataFrame({'name': one, 'lookCase': two, 'lookTimes': three, 'weightTimes': four
}) # 注:这里的one,two,three,four都是list()类型,np.array需要注明dtype
print('dataFrame=', dataFrame)
names = np.array(dataFrame['name'])
print('names one=', type(names))
return
def pandasWork2(): # 初始化,指定索引内容
keys = list(['one', 'two', 'three', 'four'])
values = list(['version', 'alisa', 'jack', 'joy'])
frame1 = pd.DataFrame(values, index= keys)
print('frame1=', frame1)
return
def pandasWork3(): # 几个字典类型的源数据,组合为一个DataFrame
dict1 = {'name1' : 'alisa', 'name2' : 'jack', 'name3' : 'Sam', 'name4' : 'Jason'} # 字典,可以作为一种源数据
dict2 = {'name1' : 18, 'name2' : 19, 'name3' : 20, 'name4' : 21}
pop = {'nmaes' : dict1, 'age' : dict2}
frame = pd.DataFrame(pop)
print('frame=', frame)
return
def pandasWork4(): # 指定索引顺序初始化
# 建立一个一维的,指定索引值,指定title的dataframe
keys = list(['one', 'two', 'three', 'four'])
values = list(['version', 'alisa', 'jack', 'joy'])
title = list(['name'])
frame = pd.DataFrame(values, index=keys,columns= title)
print('frame=', frame)
# 建立一个多维的,指定索引值,指定title即表头的顺序的dataframe
one = np.array(['version', 'alisa', 'jack', 'joy'])
one = one.reshape(len(one), 1) # 首先要不数据转为列
two = np.array([100, 200, 300, 400])
two = two.reshape(len(two), 1)
data = np.hstack((one, two))
print('data=', data.shape)
data = data.tolist()
print('list data=', data)
title1 = list(['name', 'number'])
frame1 = pd.DataFrame(data, index= keys, columns=title1)
print('frame1=', frame1)
return
#pandasWork1() # DataFrame 初始化,与数据的获取
#pandasWork2() # 初始化,指定索引内容
#pandasWork3() # 几个字典类型的源数据,组合为一个DataFrame
#pandasWork4() # 指定索引顺序初始化