wes mckinney python for data analysis

More important Ndarray object properties in an array of numpy:Ndarray.ndim: The number of dimensions (that is, the number of array axes) of the array, equal to the rank. The most common are two-dimensional arrays (matrices). ndarray.itemsize: The byte size of each element in the array. For example, an array with an element type of Float64 Itemsiz property value of 8 (float64 consumes 64 bits, each byte is 8, so 64/8, takes 8 bytes), and An array with an element type of complex32 the Item proper

(): -List1 = List (range (30000000)) -TP1 =tuple (List1) -Set1 =Set (List1) +T1 = TT. Timer ("list_time ()","From __main__ import list_time") - Print(T1.timeit (number=10)) +t2 = TT. Timer ("tuple_time ()","From __main__ import tuple_time") A Print(T2.timeit (number=10)) atT3 = TT. Timer ("set_time ()","From __main__ import set_time") - Print(T3.timeit (number=10)) - if __name__=="__main__": -Main ()Results:Description: The time complexity of the three functions is O (n), the traver

in the number rangePrint(Np.random.randint (1,10,3))#The first two digits are the range, followed by a random number of outputs#RANDN Standard dynamically generated random numbersPrint(Np.random.randn ())#0.2021606168747088Print(Np.random.randn (2,4))#normal random number of two rows of four columns" "[ [ -0.43522053 0.288716 1.5751424-0.89094638] [ -1.12602864 1.27198812-0.4784293 1.90768013]" "#choice randomly generates a random number within an arrayPrint(Np.random.choice ([10,20,30]))#rando

with mappings　　Here are just a few of the features, please refer to the official documentation for details.1Frame9 =PD. DataFrame ({2 'Item':[' Ball','Mug','Pen','Pencil','Ashtray'],3 'Color':[' White','Red','Green','Black','Yellow']4 })5 Print(FRAME9)6Price = {7 ' Ball': 5.56,8 'Mug': 4.20,9 'Bottle1': 1.30,Ten 'Scissors': 3.41, One 'Pen': 1.30, A 'Pencil': 0.56, - 'Ashtray': 2.75 - } theframe9[' Price'] = frame9['Item'].map (Price) # here is the correspondi

???IndexP.asfreq (' M ', ' Start ') #将年度数据转换为月度的形式, converted to the month of the yearP.asfreq (' M ', ' End ') #将年度数据转换为月度的形式, converted to December of the yearP1=PD. Period (' freq= ', ' A-jun ')P1.asfreq (' m ', ' Start ') #Period (' 2015-07 ', ' m ')P1.asfreq (' m ', ' End ') #Period (' 2016-06 ', ' m ')P2=PD. Period (' 2016-09 ', ' M ')P2.asfreq (' A-jun ') #2016年9月进行频率转换, equivalent to 2017 years in the time frequency ending in JuneRng=pd.period_range (' 2006 ', ' freq= ', ' A-dec ')Ts=ser

‘) #颜色深蓝cup_style = bra.groupby(‘cup‘)[‘cup‘].count() #cup列唯一值得数量cup_styleplt.figure(figsize=(8,6),dpi=80)labels = list(cup_style.index)plt.xlabel(‘cup‘) #x轴为cupplt.ylabel(‘count‘) #y轴为count数量plt.bar(range(len(labels)),cup_style,color=‘royalblue‘,alpha=0.7) #alpha为透明度plt.xticks(range(len(labels)),labels,fontsize=12)plt.grid(color=‘#95a5a6‘,linestyle=‘--‘,linewidth=1,axis=‘y‘,alpha=0.6)plt.legend([‘user-count‘])for x,y in zip(range(len(labels)),cup_style):plt.text(x,y,y,ha=‘center‘,va=‘bottom‘)co

="bfill"))‘‘‘------Back fill------One, threea-0.211055-2.869212 0.022179b-0.870090-0.878423 1.071588c-0.870090-0.878423 1.071588d-0.203259 0.315897 0.495306e-0.203259 0.315897 0.495306f 0.490568-0.968058-0.999899g 1.437819-0.370934-0.482307H 1.437819-0.370934- 0.482307 ‘‘‘Print ('------Average fill------') Print (Df.fillna (Df.mean ()))‘‘‘------Average fill------One, threea-0.211055-2.869212 0.022179b 0.128797-0.954146 0.021373c-0.870090-0.878423 1.071588d 0.128797-0.95

Operating system: Windowspython:3.5Welcome to join the Learning Exchange QQ Group: 657341423 The previous section describes the library of data analysis and mining needs, the most important of which is pandas,matplotlib.Pandas: Mainly on data analysis, calculation and statistics, such as the average, square bad.Matplot

Use Python for data analysis _ Pandas _ basic _ 2, _ pandas_2Reindex method of Series reindex In [15]: obj = Series([3,2,5,7,6,9,0,1,4,8],index=['a','b','c','d','e','f','g', ...: 'h','i','j'])In [16]: obj1 = obj.reindex(['a','b','c','d','e','f','g','h','i','j','k'])In [17]: obj1Out[17]:a 3.0b 2.0c 5.0d 7.0e 6.0f 9.0g 0.0h 1.0i 4.0j

), 'STD': List (Np.diag (np.sqrt (Res.cov_params ))),'T': List (res.tvalues),'Sig': [I forIinchMap (lambda x:float(x), ("". Join ("{:. 4f},"*len (res.pvalues)). Format (*list (res.pvalues)). Rstrip (","). Split (",")]}returnvalue= {'Model': Model,'coefficient': Coefficient}print (returnvalue){ 'Model': { 'DF':3.0, 'N':665, 'prob_f_statistic':1.185607423551511E-17, 'R_squared_adj':0.11247707470462853, 'f_statistic':29.049896130

b c D-a nan-nan nan nan-nan-nan-nan nan-nan-nan-nan NaNThe parameters of the Reindex are as follows:Deletes the item series on the specified axis (index)in []: obj = Series ([1,2,3,4],index=['a','b','C','D']) in [113]: objout[113]:a1b2C3D4dtype:int64in [[Obj1]: = Obj.drop ('C') in [115]: obj1out[115]:a1b2D4Dtype:int64DataFrameDelete a single index rowIn [109]: frameout[109]: class score0 Chinese 1201 Math 1302 English in[+]: obj = frame.drop (0) in [111]: objout[111]:

This article has shared with you about the Python data processing related content as well as the key explanation, to this knowledge point interested friend may refer to the study. Numpy, Pandas is the Python data processing often used in two frames, are written in C language, so the speed of operation. Matplotlib is a

said that the interactive way right-click and hold the date will be dynamically expanded or shrunk, actually do it, no effect ...plt.show ()>>>AA AAPL GE IBM JNJ MSFT PEP SPX XOM1990-02-01 4.98 7.86 2.87 16.79 4.27 0.51 6.04 328.79 6.121990-02-02 5.04 8.00 2.87 16.89 4.37 0.51 6.09 330.92 6.241990-02-05 5.07 8.18 2.87 17.32 4.34 0.51 6.05 331.85 6.251990-02-06 5.01 8.12 2.88 17.56 4.32 0.51 6.15 329.66 6.231990-02-07 5.04 7.77 2.91 17.93 4.38 0.51 6.17 333.75 6.33AAPL MSFT XOM1990-02-01 7.86 0

the string object method Split () method splits the string:The Strip () method removes whitespace and line breaks:Split () in combination with strip () using:The "+" symbol allows you to concatenate multiple strings together:The join () method is also the connection string, comparing it to the "+" symbol:The In keyword determines whether a string is contained in another string:The index () method and the Find () method determine the location of a substring: the difference between the index ()

;deff (x, y): -...return10*x+y + ... ->>> B = Np.fromfunction (f, (5,4), dtype=int) +>>>b AArray ([[[0, 1, 2, 3], at[10, 11, 12, 13], -[20, 21, 22, 23], -[30, 31, 32, 33], -[40, 41, 42, 43]]) ->>> b[2,3] -23 in>>> B[0:5, 1]#second column -Array ([1, 11, 21, 31, 41]) to>>> b[:, 1]#second column +Array ([1, 11, 21, 31, 41]) ->>> B[1:3,:]#second row, third row theArray ([[[10, 11, 12, 13], *[20, 21, 22, 23]]) $GT;GT;B[-1]#equivalent to B[-1,:], last linePanax NotoginsengArray ([40, 41, 42, 43])

After installing the NumPy module, I started to do a few small tests to run, but when I created numpy.py this filenumpy.pyimport numpyy = Numpy.array ([[[11,4,2],[2,6,1],[32,6,42]])print(y)After the operation error:Traceback (most recent):File "D:\Python_Reptile\numpy.py", line 1, Import NumPyFile "D:\Python_Reptile\numpy.py", line 2, y = Numpy.array ([[11,4,2],[2,6,1],[32,6,42]])Attributeerror:module ' NumPy ' has no attribute ' array 'Baidu opened the query for a long time, and the previous te

NumPy as the basis for the scientific calculation of Python, why Python is suitable for mathematical calculations, in addition to being easy to understand and easy to learn Python can simply invoke a large number of legacy libraries written in C and Fortran. The NumPy ndarray:a multidimensional Array Object Ndarray, which can be understood as an n-dimensional ar

U.S. scientists announced 11th that they first detected gravitational waves last September. This discovery confirms the prophecy of the physicist Einstein 100 years ago. Announcing the discovery was the head of the laser-interferometric gravitational Wave Observatory (LIGO). The institution was born in the 90 's and has been observed for nearly 30 years by gravitational wave observations. So the amount of gravitational wave data that is observed shou

automatically added as index Here you can simply replace index, generate a new series, People think, for NumPy, not explicitly specify index, but also can be through the shape of the index to the data, where the index is essentially the same as the numpy of the Shaping indexSo for the numpy operation, the same applies to pandas At the same time, it said that series is actually a dictionary, so you can also use a

, Dtype:float64#被聚合的只有数值列Df.groupby (df[' key1 '). Mean ()OUT[19]:Data1 data2Key1A 0.262833 0.370314b 0.246574 0.606039Df.groupby ([' Key1 ', ' Key2 ']). Mean ()OUT[20]:Data1 data2Key1 Key2A one-0.230076 0.4970981.248653 0.116745b one-0.196613-0.2241980.689761 1.436277For Name,group in Df.groupby ([' Key1 ']):Print (name)Print (group)AData1 data2 Key1 Key20-0.169761-0.297803 a One1 1.248653 0.116745 A4-0.290392 1.292000 a OneBData1 data2 Key1 Key22-0.196613-0.224198 B One3 0.689761 1.436277 bfor