#Pandas
'''
1,Pandas是Python的一個數據分析報包,該工具爲解決數據分析任務而創建。
2,Pandas納入大量庫和標準數據模型,提供搞笑的操作數據集所需的工具
3.pandas提供大量能使我們快速便捷地處理數據的1函數方法
4,Pandas是字典形式,基於Numpy創建,讓Numpy爲中心的應用變得更加簡單
'''
import pandas as pd
import numpy as np
#4 Pandas 數據結構
#4.1Series
s = pd.Series([1,2,3,np.nan,5,6])#索引在左邊值在右邊
print(s)
#4.2 Date Frame
#DateFrame是表格型數據結構,包含一組有序的列,每列可以使不同的值類型。DateFrame有行索引和列索引,可以看成由Series組成的字典。
dates = pd.date_range('20180310',periods = 6)
df = pd.DataFrame(np.random.rand(6,4),index=dates,columns=['A','B','C','D'])
print(df)
print(df['B'])
#創建特定數據的DataFrame
df_1 = pd.DataFrame({
'A':1.,
'B':pd.date_range('20180923',periods=4),
'D':np.array([2]*4,dtype='int32'),
'E':pd.Categorical(['test','train','test','train']),
'F':'foo'
})
#
print(df_1)
print(df_1.dtypes)
print(df_1.index)#行的序號
print(df_1.columns)#列的序號
print(df_1.values)#把每個值進行打印
print(df_1.describe())#數字總結
print(df_1.T)#數字反轉
print(df_1.sort_index(axis=1,ascending=False))#axis等於按第一列排序,如ABCDEFG,然後ascending倒序進行顯示
print(df_1.sort_values(by='E'))#按值進行排列
#pandas選擇數據
dates = pd.date_range('20180924',periods=6)
df = pd.DataFrame(np.random.rand(6,4),index=dates,columns=['A','B','C','D'])
print(df)
print(df[0:3],df['20180910':'20180926'])#第一次切片選擇,第二次按照篩選條件選擇
print(df.loc['20180924',['A','B']])#按照行標籤進行選擇
print(df.iloc[3,1])#輸出第三行第一列的數據
print(df.iloc[3:5,0:2])#3,5行,0,3列
print(df.iloc[[1,2,4],[0,2]])#不連續篩選
print(df[df.A > 0])#篩選出df.A大於0的元素
#pandas設置數據
datas = pd.date_range('20180310',periods=6)
df = pd.DataFrame(np.arange(24).reshape(6,4),index=datas,columns=['A','B','C','D'])
print(df)
df.iloc[2,2] = 999
df.loc['2018-03-15','D'] = 999
print(df)
df[df.A > 0] = 999#A列大於0的爲999???
print(df)
df['F'] = np.NAN
print(df)
df['E'] = pd.Series([1,2,3,4,5,6],index=pd.date_range('20180310',periods=6))#添加一列
print(df)
#7Pandas處理數據
dates = pd.date_range('20180310',periods=6)
df = pd.DataFrame(np.arange(24).reshape(6,4),index=dates,columns=['A','B','C','D'])
df.iloc[0,1]=np.nan
df.iloc[1,2]=np.nan
print(df)
print(df.dropna(axis=0,how='any'))#0對行進行操作 1對列進行操作 any:只要存在NaN即可drop掉 all:必須全部是NaN纔可drop
print(df.fillna(value=0))#將NaN值替換爲0
print(pd.isnull(df))#是nan爲true不是nan爲false
print(np.any(df.isnull()))#判斷數據中是否存在nanz值
#8 pandas的導入導出
data = pd.read_csv('test1.csv')
data.to_pickle('test.pickle')#將資料存取成pickle文件
#9.pandas合併數據
df1 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2,columns=['a','b','c','d'])
#
res = pd.concat([df1,df2,df3],axis=0,ignore_index=True)#0表示行合併,1表示列合併,ingnore_index重置序列index index變爲1-8
print(res)
#join合併
df1 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d'],index=[1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d'],index=[2,3,4])
print(df1)
print(df2)
res = pd.concat([df1,df2],axis=1,join='outer')#行往外合併
print(res)
res = pd.concat([df1,df2],axis=1,join_axes=[df1.index])#以df1的序列進行合併,df2中沒有的序列NAN值填充
print(res)
#append添加
df1 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2,columns=['a','b','c','d'])
s1 = pd.Series([1,2,3,4],index=['a','b','c','d'])
res = df1.append(df2,ignore_index=True)#將df2合併到df1下面並重置index
print(res)
res = df1.append(s1,ignore_index=True)#將s1合併到df1下面並重置index
print(res)
#pandas和並merge
#依據一組key合併
left = pd.DataFrame({
'key':['k1','k2','k3','k4'],
'A':['A1','A2','A3','A4'],
'B':['B1','B2','B3','B4']
})
#
print(left)
#
right = pd.DataFrame({
'key':['k1','k2','k3','k4'],
'C':['C1','C2','C3','C4'],
'D':['D1','D2','D3','D4']
})
#
print(right)
#
res = pd.merge(left,right,on = 'key')
print(res)
#依據兩組key合併
left = pd.DataFrame({
'key':['k0','k0','k1','k2'],
'key2':['k0','k1','k0','k1'],
'A':['A1','A2','A3','A4'],
'B':['B1','B2','B3','B4']
})
#
right = pd.DataFrame({
'key':['k0','k1','k1','k2'],
'key2':['k0','k0','k0','k0'],
'C':['C1','C2','C3','C4'],
'D':['D1','D2','D3','D4']
})
print(left)
print(right)
res = pd.merge(left,right,on=['key','key2'],how='inner')#how = outer left right
print(res)
#indicator合併
df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})
df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
print(df1)
print(df2)
res = pd.merge(df1,df2,on='col1',how='outer',indicator=True)#依據col1進行合併 並啓用indicator = True輸出沒想合併式
print(res)
res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column')#自定義indicator column名稱
print(res)