python学习之pandas

#Pandas ''' 1,Pandas是Python的一个数据分析报包，该工具为解决数据分析任务而创建。 2，Pandas纳入大量库和标准数据模型，提供搞笑的操作数据集所需的工具 3.pandas提供大量能使我们快速便捷地处理数据的1函数方法 4，Pandas是字典形式，基于Numpy创建，让Numpy为中心的应用变得更加简单 ''' import pandas as pd import numpy as np #4 Pandas 数据结构 #4.1Series

s = pd.Series([1,2,3,np.nan,5,6])#索引在左边值在右边

print(s)

#4.2 Date Frame #DateFrame是表格型数据结构，包含一组有序的列，每列可以使不同的值类型。DateFrame有行索引和列索引，可以看成由Series组成的字典。

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'])

#创建特定数据的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)#行的序号

print(df_1.columns)#列的序号

print(df_1.values)#把每个值进行打印

print(df_1.describe())#数字总结

print(df_1.T)#数字反转

print(df_1.sort_index(axis=1,ascending=False))#axis等于按第一列排序，如ABCDEFG，然后ascending倒序进行显示

print(df_1.sort_values(by='E'))#按值进行排列

#pandas选择数据

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'])#第一次切片选择，第二次按照筛选条件选择

print(df.loc['20180924',['A','B']])#按照行标签进行选择

print(df.iloc[3,1])#输出第三行第一列的数据

print(df.iloc[3:5,0:2])#3,5行，0,3列

print(df.iloc[[1,2,4],[0,2]])#不连续筛选

print(df[df.A > 0])#筛选出df.A大于0的元素

#pandas设置数据

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列大于0的为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处理数据

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对行进行操作 1对列进行操作 any：只要存在NaN即可drop掉 all:必须全部是NaN才可drop

print(df.fillna(value=0))#将NaN值替换为0

print(pd.isnull(df))#是nan为true不是nan为false

print(np.any(df.isnull()))#判断数据中是否存在nanz值

#8 pandas的导入导出

data = pd.read_csv('test1.csv')

data.to_pickle('test.pickle')#将资料存取成pickle文件

#9.pandas合并数据

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表示行合并，1表示列合并，ingnore_index重置序列index index变为1-8

print(res)

#join合并

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')#行往外合并

print(res)

res = pd.concat([df1,df2],axis=1,join_axes=[df1.index])#以df1的序列进行合并，df2中没有的序列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)#将df2合并到df1下面并重置index

print(res)

res = df1.append(s1,ignore_index=True)#将s1合并到df1下面并重置index

print(res)

#pandas和并merge

#依据一组key合并

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)

#依据两组key合并

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合并

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)#依据col1进行合并 并启用indicator = True输出没想合并式

print(res)

res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column')#自定义indicator column名称 print(res)