python for data analysis 2nd edition

If you are not a python based classmate, it is recommended to download the installation Anaconda directly, which has integrated a variety of data analysis required modules, here do not repeat. Download Address: https://www.continuum.io/downloads/ Here's how to install and use Python's pip to install each module method, Pip is a tool for installing and managing

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

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)

]: B=np.ones ([3,4])#generate all 1 arrays - +in [5]: b -Out[5]: +Array ([[1., 1., 1., 1.], A[1., 1., 1., 1.], at[1., 1., 1., 1.]]) - -In [6]: C=np.random.rand (3,4)#generating a random array - -in [7]: C -Out[7]: inArray ([[[0.36417168, 0.24336724, 0.78826727, 0.42894367], -[0.77198615, 0.95897315, 0.25628233, 0.53995372], to[0.02777746, 0.25093856, 0.14544893, 0.10475779]]) + -In [8]: D=np.eye (3)#Generating a unit array the *in [9]: D $Out[9]:Panax NotoginsengArray ([[1., 0., 0.], -[0.,

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Introduction URL: Https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebookImport Matplotlib.pyplot as PltImport Seaborn as SNSImport Pandas as PDImport data:Iris=pd.read_csv (' E:\\data\\iris.csv ')Iris.head ()To make a histogram:Plt.hist (iris[' SEPALLENGTHCM '],bins=15)Plt.xlabel (' SEPALLENGTHCM ')Plt.ylabel (' quantity ')Plt.title ('

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

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

???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

._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

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