Pandas 10分钟入门(官方说明+个人小测试)
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import pandas as pdimport numpy as npimport matplotlib.pyplot as pltIn [20]:
s= pd.Series([1,2,3,np.nan,5,6,]) #series 类型数组。sOut[20]:
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dates= pd.date_range("20170112",periods=6) #Creating a DataFrame by passing a numpy array, with a datetime index and labeled columndatesOut[21]:
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list(dates)dates.dateOut[22]:
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list(dates.date)Out[23]:
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dates.yearOut[24]:
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list(dates.year)Out[25]:
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list(dates.day)Out[26]:
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str(dates.date)Out[27]:
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df=pd.DataFrame(np.random.randn(6,4),index=dates,columns=list("ABCD"))dfOut[28]:
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df2 = pd.DataFrame({ 'A' : 1., 'B' : pd.Timestamp('20130102'), 'C' : pd.Series(1,index=list(range(4)),dtype='float32'), 'D' : np.array([3] * 4,dtype='int32'), 'E' : pd.Categorical(["test","train","test","train"]), 'F' : 'foo' }) #Creating a DataFrame by passing a dict of objects that can be converted to series-like.df2Out[29]:
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df2.dtypesOut[30]:
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df.dtypesOut[31]:
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df2.<TAB> #使用jupyter时按tab键，可以看到代码提示。In [36]:
df.head()Out[36]:
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df.indexOut[37]:
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df.columnsOut[38]:
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df.valuesOut[39]:
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df.describe()Out[40]:
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dfOut[41]:
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df.TOut[42]:
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df.sort_index(axis=1,ascending=False) #Sorting by an axis 排序。Out[43]:
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df.sort_values(by="B") #Sorting by valuesOut[44]:
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dfOut[45]:
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df["A"]# Selecting a single column, which yields a Series, equivalent to df.AOut[46]:
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df.AOut[47]:
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df[0:3] #Selecting via [], which slices the rows.Out[48]:
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df["2017-01-13":"2017-01-17"]Out[49]:
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datesOut[50]:
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df.loc[dates[0]] #For getting a cross section using a labelOut[51]:
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df.loc[:,["A","B"]]Out[52]:
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df.loc['20170112':'20170116',['A','B']] #Showing label slicing, both endpoints are includedOut[53]:
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df.loc["20170115",["A","B"]] Out[54]:
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df.loc[dates[3],"D"] #For getting a scalar valueOut[55]:
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df.at[dates[3],"D"] #For getting fast access to a scalar (equiv to the prior method)Out[56]:
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df.iloc[3] #Select via the position of the passed integersOut[57]:
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df.iloc[2:5,0:2] # By integer slices, acting similar to numpy/pythonOut[58]:
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df.iloc[[1,3,4],[0,2]] #By lists of integer position locations, similar to the numpy/python styleOut[59]:
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df.iloc[1:3,:]Out[60]:
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df.iloc[:,1:3]Out[61]:
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df.iloc[1,1] #For getting a value explicitlyOut[62]:
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df.iat[1,1] #For getting fast access to a scalar (equiv to the prior method)Out[63]:
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df[df.A>0] #Using a single column’s values to select dataOut[64]:
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df[df>0] #Selecting values from a DataFrame where a boolean condition is metOut[65]:
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df2Out[66]:
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dfOut[67]:
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df2=df.copy()df2Out[68]:
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df.equals(df2)Out[69]:
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df==df2Out[70]:
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df is df2Out[71]:
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df2["E"]=["one","one","two","three","four","three"]df2Out[72]:
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df2[df2.E.isin(["two","four"])]Out[73]:
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df2[df2["E"].isin(["two","four"])]Out[74]:
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s1= pd.Series([1,2,3,4,5,6],index=pd.date_range("20171016",periods=6)) #Setting a new column automatically aligns the data by the indexess1Out[75]:
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df.at[dates[0],"A"]=0 #Setting values by labelIn [77]:
dfOut[77]:
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df.iat[0,1]=0dfOut[78]:
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df.loc[:,"D"]=np.array([5]*len(df)) #Setting by assigning with a numpy arraydfOut[79]:
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df2=df.copy()df2Out[80]:
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df2[df2>0]=-df2df2Out[81]:
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dfOut[83]:
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df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])df1.loc[dates[0]:dates[1],'E'] = 1df1Out[84]:
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df1.dropna(how="any") #To drop any rows that have missing dataOut[85]:
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df1.fillna(value=5) # Filling missing dataOut[86]:
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df1Out[87]:
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pd.isnull(df1)Out[88]:
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df1.isnull()Out[89]:
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df1.isna() #没有这个方法~~In [91]:
dfOut[91]:
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df.mean()Out[92]:
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df.mean(1) #Same operation on the other axisOut[93]:
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s= pd.Series([1,2,3,np.nan,4,5],index=dates).shift(2) # Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically broadcasts along the specified dimension.sOut[94]:
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dfOut[95]:
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df.sub(s,axis="index") #dataFrame与series的减法Out[96]:
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dfOut[97]:
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df.apply(np.cumsum) #行叠加。Out[98]:
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df.apply(lambda x: x.max()-x.min())Out[99]:
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s= pd.Series(np.random.randint(0,7,size=10))sOut[100]:
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s.value_counts()Out[101]:
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s= pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])s.str.lower()Out[102]:
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sOut[103]:
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dfOut[104]:
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df=pd.DataFrame(np.random.randn(10,4))dfOut[105]:
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# break it into piecespieces=[df[:3],df[3:7],df[7:]]pd.concat(pieces)Out[106]:
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piecesOut[107]:
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left=pd.DataFrame({"key":["foo","foo"],"lval":[1,2]})right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})In [109]:
leftOut[109]:
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rightOut[110]:
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pd.merge(left,right,on="key")Out[111]:
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left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})In [113]:
leftOut[113]:
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rightOut[114]:
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pd.merge(left,right,on="key")Out[115]:
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df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])dfOut[116]:
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s=df.iloc[3]sOut[117]:
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df.append(s,ignore_index=True)Out[118]:
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df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], 'B' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], 'C' : np.random.randn(8), 'D' : np.random.randn(8)})dfOut[119]:
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df.groupby("A").sum()Out[120]:
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df.groupby(["A","B"]).sum() #Grouping by multiple columns forms a hierarchical index, which we then apply the function.Out[121]:
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tuples = list(zip([['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]))tuplesOut[122]:
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tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]))tuplesOut[123]:
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index=pd.MultiIndex.from_tuples(tuples,names=["first","second"])indexOut[124]:
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df=pd.DataFrame(np.random.randn(8,2),index=index,columns=['A', 'B'])dfOut[125]:
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df2=df[:4]df2Out[126]:
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stacked= df2.stack()stackedOut[127]:
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stacked.unstack()Out[128]:
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stacked.unstack(1)Out[129]:
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stacked.unstack(0)Out[130]:
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df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3, 'B' : ['A', 'B', 'C'] * 4, 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2, 'D' : np.random.randn(12), 'E' : np.random.randn(12)})dfOut[131]:
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pd.pivot_table(df,values="D",index=["A","B"],columns=["C"])Out[4]:
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rng=pd.date_range("1/2/2017",periods=100,freq="S")rngOut[132]:
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ts =pd.Series(np.random.randint(0,500,len(rng)),index=rng)tsOut[133]:
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ts.resample("5Min").sum()Out[7]:
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ts.resample("1Min").sum()Out[9]:
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rng= pd.date_range("2/1/2017 00:00",periods=5,freq="D")rngOut[10]:
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ts=pd.Series(np.random.randn(len(rng)),index=rng)tsOut[12]:
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tsUtc=ts.tz_localize("UTC")tsUtcOut[13]:
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tsUtc.tz_convert("US/Eastern")Out[14]:
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tsUtcOut[15]:
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rng=pd.date_range("1/8/2017",periods=5,freq="M")rngOut[16]:
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ts=pd.Series(np.random.randn(len(rng)),rng)tsOut[18]:
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ps=ts.to_period()psOut[20]:
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ps.to_timestamp()Out[21]:
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psOut[22]:
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prng=pd.period_range("1990Q1","2017Q4",freq="Q-NOV")prngOut[23]:
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ts= pd.Series(np.random.randn(len(prng)),prng)ts.head()Out[25]:
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ts.index=(prng.asfreq("M","e")+1).asfreq("H","s")+9ts.head()Out[26]:
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df = pd.DataFrame({"id":[1,2,3,4,5,6],"raw_grade":["a","a","c","b","b","f"]})dfOut[34]:
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df["grade"]=df.raw_grade.astype("category")dfOut[35]:
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df.grade #Convert the raw grades to a categorical data typeOut[36]:
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# Rename the categories to more meaningful names (assigning to Series.cat.categories is inplace!)df.grade.cat.categories=["very good","good","nomal","bad"]dfOut[37]:
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# Reorder the categories and simultaneously add the missing categories (methods under Series .cat return a new Series per default).df.grade=df.grade.cat.set_categories(["very bad", "bad", "medium","good", "very good"])df.gradeOut[38]:
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dfOut[39]:
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df.sort_values(by="grade")Out[40]:
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df.groupby("grade").size()Out[41]:
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ts=pd.Series(np.random.randn(1000),index=pd.date_range("1/1/2017",periods=1000))ts.head()Out[43]:
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ts=ts.cumsum()ts.head()Out[45]:
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ts.plot()Out[48]:
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df=pd.DataFrame(np.random.randn(1000,4),index=ts.index,columns=["A","B","C","D"])df.head()Out[50]:
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df=df.cumsum()In [53]:
plt.figure()df.plot()plt.legend(loc="best")plt.show()In [ ]:
df.to_csv("foo.csv")In [ ]:
pd.read_csv("foo.csv")In [ ]:
df.to_hdf("foo.h5","df")In [ ]:
pd.read_hdf("foo.h5","df")In [ ]:
df.to_excel('foo.xlsx', sheet_name='Sheet1')In [ ]:
pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])In [ ]:
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