利用python进行数据分析--pandas入门2

随书练习，第五章  pandas入门2

# coding: utf-8

# In[1]:


from pandas import Series,DataFrame
import pandas as pd
import numpy as np


# In[2]:


obj = Series(range(5),index=['a','a','b','b','c'])


# In[3]:


obj #带有重复索引的Series


# In[4]:


obj.index.is_unique


# In[5]:


obj['a']


# In[6]:


obj['c']


# In[7]:


df = DataFrame(np.random.randn(4,3),index=['a','a','b','b'])


# In[8]:


df


# In[9]:


df.loc['b']


# In[10]:


df = DataFrame([[1.4,np.nan],[7.1,-4.5],[np.nan,np.nan],[0.75,-1.3]],index=['a','b','c','d'],columns=['one','two'])


# In[11]:


df


# In[12]:


df.sum()


# In[13]:


df.sum(axis=1) #自动忽略缺失值


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df.mean(axis=1,skipna=False) #禁用自动忽略缺失值功能


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df.idxmax() #间接统计，返回索引


# In[16]:


df.idxmax(axis=1)


# In[17]:


df.cumsum() #累计型


# In[18]:


df.describe() #数值型数据


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obj = Series(['a','a','b','c']*4)


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obj


# In[21]:


obj.describe() #非数值型数据


# In[22]:


# corr ,cov ,corrwith


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obj = Series(['c','a','d','a','a','b','b','c','c'])


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uniques = obj.unique()  #这里只是找出唯一值，不排序，numpy有一个函数unique()去重并排序


# In[25]:


uniques


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uniques.sort()


# In[27]:


uniques


# In[28]:


obj.value_counts() #用于计算各值出现的频数，降序排列


# In[29]:


pd.value_counts(obj.values,sort=False)


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mask = obj.isin(['b','c'])


# In[31]:


mask


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obj[mask] #布尔型索引


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data = DataFrame({'Qu1':[1,3,4,3,4],'Qu2':[2,3,1,2,3],'Qu3':[1,5,2,4,4]})


# In[34]:


data


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result = data.apply(pd.value_counts).fillna(0)


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result  #索引是唯一值，列是唯一值出现的次数


# In[37]:


string_data = Series(['aardvark','artichoke',np.nan,'avocado'])


# In[38]:


string_data


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string_data.isnull()


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string_data[0] = None


# In[41]:


string_data.isnull() #python内置的None值也会被当作NA值处理


# In[42]:


data = DataFrame({'Qu1':[np.nan,3,np.nan,3,4],'Qu2':[2,3,np.nan,2,3],'Qu3':[np.nan,5,np.nan,4,np.nan]})


# In[43]:


data


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data.dropna() #默认删除有缺失值的数据


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data.dropna(how='all')


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data.dropna(thresh=2) #缺失数量大于等于2的行删掉


# In[47]:


data.notnull() #isnull的否定式


# In[48]:


from numpy import nan as NA


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data = Series([1,NA,3.5,NA,7])


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data.dropna()


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data[data.notnull()] #布尔型索引


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data = DataFrame([[1,6.5,3],[1,NA,NA],[NA,NA,NA],[NA,6.5,3]])


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cleaned = data.dropna()


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data


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cleaned


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data[4] = NA


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data


# In[58]:


data.dropna(axis=1,how='all')


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df = DataFrame(np.random.randn(7,3))


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df.iloc[:4,1] = NA;df.iloc[:2,2] = NA


# In[61]:


df


# In[62]:


df.fillna(0)


# In[63]:


df.fillna({1:0.5,3:-1}) #可以对不同的列填充不同的值


# In[64]:


_ = df.fillna(0,inplace=True) #会改变原数据


# In[65]:


df


# In[66]:


df = DataFrame(np.random.randn(6,3))


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df.iloc[2:,1] = NA;df.iloc[4:,2] = NA


# In[68]:


df


# In[69]:


df.fillna(method='ffill')


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df.fillna(method='ffill',limit=2)


# In[71]:


data = Series([1,NA,3.5,NA,7])


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data.fillna(data.mean())


# In[73]:


df


# In[74]:


df.mean()


# In[75]:


df.fillna(df.mean()) #用平均值填充


# In[76]:


df.iloc[3,1]=5


# In[77]:


df


# In[78]:


df.interpolate() #插值法填充缺失值


# In[79]:


data = Series(np.random.randn(10),index=[['a','a','a','b','b','b','c','c','d','d'],[1,2,3,1,2,3,1,2,2,3]]) #层次化索引


# In[80]:


data


# In[81]:


data.index


# In[82]:


data['b']


# In[83]:


data['b':'c']


# In[84]:


data[['b','d']]


# In[85]:


data.loc[['b','d']]


# In[86]:


data[:,2]


# In[87]:


data.unstack()


# In[88]:


data.unstack().stack()


# In[89]:


frame = DataFrame(np.arange(12).reshape((4,3)),index=[['a','a','b','b'],[1,2,1,2]],columns=[['Ohio','Ohio','Colorado'],['Green','Red','Green']])


# In[90]:


frame


# In[91]:


frame.index.names = ['key1','key2']


# In[92]:


frame.columns.names = ['state','color']


# In[93]:


frame


# In[94]:


frame['Ohio']


# In[103]:


pd.MultiIndex.from_arrays([['Ohio','Ohio','Colorado'],['Green','Red','Green']],names=['state','color'])


# In[104]:


frame.swaplevel('key1','key2') #互换级别


# In[106]:


frame.sort_index(level=1)


# In[109]:


frame.swaplevel(0,1).sort_index(level=0)


# In[110]:


frame


# In[111]:


frame.sum(level='key2') # 根据级别进行汇总


# In[112]:


frame.sum(level='color',axis=1)


# In[113]:


frame = DataFrame({'a':range(7),'b':range(7,0,-1),'c':['one','one','one','two','two','two','two'],'d':[0,1,2,0,1,2,3]})


# In[114]:


frame


# In[115]:


frame2 = frame.set_index(['c','d']) #将一个或多个列转换为行索引


# In[116]:


frame2


# In[117]:


frame.set_index(['c','d'],drop=False)


# In[118]:


frame2


# In[119]:


frame2.reset_index()


# In[125]:


frame2


# In[124]:


frame.loc[frame.d>0]