python for data analysis 2nd edition download

Use Python to connect to the central control attendance machine. Download and analyze the data and send the results to the personnel.Central Control SDK Package: x32 Address x64 AddressSDK Package recommended 32-bit, on the Win7 64-bit system with 64-bit development package does not, with 32 can.Python also pywin32 note version, I use the 32-bit

methodRanking:Rank ()Axis index with duplicate valuesThe Is_unique () property of the index can tell you if its value is uniqueSummary and calculation of descriptive statisticsSUM ()Mean ()Describe ()Describing and summarizing statistical functionscorrelation coefficients and covarianceThe series and Dataframe methods are computed for the parameter pairs.Unique value, value count, and membershipUnique value: Unique () methodValue count: The Value_counts () method calculates how often each value

Fourth NumPy basics: arrays and vector calculations To be honest, the main purpose of using NumPy is to apply vectorization operations. NumPy does not have much advanced data analysis capabilities, and understanding numpy and array-oriented computations can help to understand the pandas behind it. According to the textbook, the author's concern is mainly focused on: Fast vectorization operations f

???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

The Ggplot library used in Python in data analysis can be applied to drawData, for example, using data from the course VII of the InstituteData is: https://s3.amazonaws.com/content.udacity-data.com/courses/ud359/hr_year.csv Scatter plot: gp=pandas.read_csv (hr_year_csv) GG=ggplot (Gp,aes ('yearid','HR ')

pandas:powerful Python Data Analysis Toolkit Official document: http://pandas.pydata.org/pandas-docs/stable/1. Import Package PandasImport Pandas as PD　　2. Get the file name under the folderImport osfilenames=[]Path= "C:/users/forrest/pycharmprojects/test" for file in Os.listdir (path): filenames.append (file)　　3. Read the first few lines of files (. csv file)

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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]:

NumPy modules can efficiently process data, provide array support, and many modules rely on him, such as: Pandas, SciPy, matplotlibInstalling NumPyFirst to the website: https://www.lfd.uci.edu/~gohlke/pythonlibs/Find NUMPY+MKL My Python version is 3.6.1, the system is 64-bit So the package that corresponds to the download is: After downloading the package, go t

broadcasts.Basic indexes and slicesLike a list in Python, an array slice is a view of the original array.Arr[0][2]arr[0,2] These two are the sameBoolean indexYou can use! =,-, or ,| to perform the operation.Fancy IndexRefers to the use of an integer array for indexing.Array Transpose and AxisymmetricArr. TNp.dot (arr. T,arr) Calculating the inner productThe transpose of the high-level array is not quite clear.There is also a swapaxes method that need

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 first two articles briefly introduce some common methods of scientific computing numpy, and some other content that will be learned in later examples. Another module,--matplotlib, is described below.Matplotlib is a Python 2D drawing library that tries to make complex drawing visualizations easier. A few lines of code can generate drawings, histograms, power spectra, bar charts, error plots, scatter plots and other 2D graphics, which we often use

._clean_fields () self._clean_form () Self._post_clean ( )Start validation field: Self._clean_fields ()def _clean_fields (self):#循环字段, the field that is set in the form component, which is from the __new__ of Declarativefieldsmetaclass forName, fieldinchSelf.fields.items (): # value_from_datadict () gets the data fromThe data dictionaries. # Each widget type knows what to retrieve it own

This script reads SQL Server, just given the table name or view name, and if there is data, it will output each data distribution map that meets the requirements for each field.#-*-coding:utf-8-*-#python 3.5.0#Exploratory Analytics (exploratory data Analysis,eda)__author__='

,:]A[:,:,::2] The last dimension is step 2Operation of NdarrayScalar operations1 each element in the array is calculated with itA=a/a.mean ()Scalar elementsNp.abs (x)Np.fabs ()NP.SQRT ()Np.squar ()Np.log () np.log10 () np.log2 ()Np.ceil () Np.floor ()Np.rint () roundingNP.MODF () returns the decimal and integer numbers of the array as two separate arraysNp.cos cosh sin sinh tan tanhNp.exp ()Np.sign ()+-*/**Np.maximum (x, y) Np.fmax ()Np.minimum (x, y) np.fmin () to find the corresponding maximum

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 ()