3、索引、选取和过滤
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4、算术运算和数据对齐
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由上面可以看到，自动的数据对齐操作在不重叠的索引处引入NA值。
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从上面看出，相加后会返回一个新的DataFrame，其索引和列为原来两个DataFrame的并集。
5、在算术方法中填充值
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6、DataFrame和Series之间的运算
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行廣播.png
列廣播.png
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源码如下：
# In[1]:
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
s1 = Series([2,9,7],index=['a','b','c'])
print(s1)
# In[2]:
s2 = Series([5,7,11,3],index=['a','c','g','f'])
print(s2)
# In[3]:
# 相加
sm = s1 + s2
print (sm)
# In[4]:
df1 = DataFrame(np.arange(9).reshape((3,3)),columns=list('bcd'),
index=['Guangdong','Shandong','Henan'])
print(df1)
# In[5]:
df2 = DataFrame(np.arange(12).reshape((4,3)),columns=list('bde'),
index=['Hainan','Shandong','Henan','Anhui'])
print(df2)
# In[6]:
sm2 = df1 + df2
print(sm2)
# In[7]:
df1 = DataFrame(np.arange(12).reshape((3,4)),columns=list('abcd'))
print(df1)
# In[8]:
df2 = DataFrame(np.arange(20).reshape((4,5)),columns=list('abcde'))
print(df2)
# In[9]:
df = df1 + df2
print(df)
# In[10]:
ad = df1.add(df2,fill_value=0)
print(ad)
# In[11]:
re = df1.reindex(columns=df2.columns,fill_value=0)
print(re)
# In[12]:
arr = np.arange(12).reshape((3,4))
print(arr)
# In[13]:
print(arr[0])
# In[14]:
di = arr - arr[0]
print(di)
# In[15]:
frame = DataFrame(np.arange(12).reshape((4,3)),columns=list('bde'),
index=['Guangdong','Shandong','Henan','Sichuan'])
print(frame)
# In[16]:
series = frame.ix[0]
print(series)
# In[17]:
fr_se = frame - series
print(fr_se)
# In[18]:
series2 = Series(range(3),index=['b','e','f'])
fr_se2 = frame + series2
print(fr_se2)
# In[19]:
# 匹配行在列上广播
print(frame)
# In[20]:
series3 = frame['d']
print(series3)
# In[21]:
# sub表示减法，即frame-series3
# axis=0表示传入的轴号是需要匹配的轴
su = frame.sub(series3,axis=0)
print(su)
# In[22]:
obj = Series(np.arange(4),index=['a','b','c','d'])
print(obj)
# In[23]:
# 索引具体标签后的值
print(obj['b'])
# In[24]:
# 索引整数等价于obj['b']
print(obj[1])
# In[25]:
# 类似于NunmPy数组的索引
print(obj[1:3])
# In[26]:
print(obj[[1,3]])
# In[27]:
print(obj[['b','a','d']])
# In[28]:
print(obj[obj<3])
# In[29]:
print(obj['b':'c'])
# In[30]:
# 赋值
obj['b':'c'] = 5
print(obj)
# In[32]:
# DataFrame索引
data = DataFrame(np.arange(16).reshape((4,4)),
index=['Guangdong','Shandong','Henan','Sichuan'],
columns=['one','two','three','four'])
print(data)
# In[33]:
print(data['two'])
# In[34]:
print(data[['three','one']])
# In[35]:
# 通过切片选取行
print(data[:2])
# In[36]:
# 通过布尔型数组选取行
bool = data[data['three'] > 5]
print(bool)
# In[37]:
# 通过布尔型DataFrame进行索引:
print(data < 5 )
# In[38]:
# 通过布尔型DataFrame结果进行赋值
data[data < 5] = 0
print(data)
# In[40]:
print(data)
# In[41]:
# 引入索引字段ix
i = data.ix['Henan',['two','four']]
print(i)
# In[42]:
i2 = data.ix[['Shandong','Sichuan'],[1,2,3]]
print(i2)
# In[43]:
# 引入索引字段ix索引行
i3 = data.ix[3]
print(i3)
# In[46]:
# 使用ix索引一定范围
i4 = data.ix[:'Henan','three']
print(i4)
# In[47]:
i5 = data.ix[data.three > 5, :3]
print(i5)
