python data analysis coursera

, the normal function can generate a sample array of 4*4: Samples = np.random.normal (size = (bis)) samplesout[]: Array ([[-1.22102285, 2.08688133, 1.15874399, 0.14342708], [-0.29772372, 0.36137871, 0.60243437, -0.09287792], [-0.49263459, 0.69445334, 1.02035894, -1.18263174], [-0.07184985,- 1.11834445, 0.89547984, 0.0585053]]) 3. ExampleRandom Walk 1000:nsteps = np.random.randint (0,2,size= Np.where (draws>0,1,-1= steps.cum

This blog is used to record learning and facilitate review review.PyplotIn the Matplotlib object-oriented drawing library, Pyplot is a convenient interface.Basic drawing functionsMp.plot (array of horizontal coordinates, array of vertical coordinates) 1 from __future__ import unicode_literals 2 import NumPy as NP 3 import Matplotlib.pyplot as MP 4 x = Np.linspace (-np.pi, Np.pi, 1000) 5 cos_y = Np.cos (x)/26 sin_y = Np.sin (x) 7 mp.plot (x, cos_y) 8 mp.plot (x, sin_y) 9 m

# mean averaging # std standard deviation # var asks for variance # min to find minimum # Max to find maximum value # argmin Minimum index # argmax Max indexXi. NumPy: Random number generationRandom number generation function within the Np.random sub-packageCommon functions: # Rand Given shape produces a random array (number between 0 and 1)# randint a given shape produces a random integer # Choice The given shape produces a random selection # Shuffle

This paper illustrates the data transmission UDP implementation method of Python network programming. Share to everyone for your reference. The specific analysis is as follows: First, the question: Do you think that tools like msn,qq on the Web transmit data mysteriously between machines? You want to play a little bi

] # - ifPattern.search (Invoice_number): # Use the RE module's search function to find patterns in the value of Invoice_number -Filewriter.writerow (row_list) # If the pattern appears in Invoice_number, write the line to the output file Pandas 1 #! /usr/bin/env Python32 3 ImportPandas as PD4 ImportSYS5 6Input_file = sys.argv[1]7Output_file = sys.argv[2]8 9Data_frame =pd.read_csv (input_file)TenData_frame_value_matches_pattern = data_frame.loc[data_frame['Invoice

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 Python 2.7 and th

which one is best for use, so let's review it.Concat allows the user to append one or more dataframe (depending on how you define the axis) below or next to the table.Merge merges multiple dataframe to specify the same row as the primary key (key).Join, like merge, incorporates two dataframe. But it does not merge by a specified primary key, but is merged by the same column name or row name.Pandas ApplyApply is designed for the pandas series. If you're not familiar with series, you can think of

)Run Result: [' 1 ' 2 ' 5 ' 11 '](2) NumPy create a two-dimensional arraySyntax: Numpy.array ([[Element 1, Element 2,..., element n],[element 1, Element 2,..., element n],..., [element 1, Element 2,..., element N]])Import= Numpy.array ([[11,4,2],[2,6,1],[32,6,42]])print(y)Operation Result:[[11 4 2][2 6 1][32 6 42]](3) Sort by using sortImportNumPy#Numpy.array ([element 1, Element 2,..., element n])x = Numpy.array (["m","2","5"," One"])#sort xX.sort ()Print(x)#Numpy.array ([[Element 1, Element 2,

areas of the drawing method (one is using the above column chart that way Fig,ax = Plt.subplots), the other is the following, this can be customized to occupy the number of spaces)Fig = plt.figure () Ax1 = Plt.subplot2grid ((2,3), (0,0)) Ax1.bar (data_bar.index,data_bar.values) fig.set_size_inches ( 12,6) Ax2 = Plt.subplot2grid ((2,3), (0,1), colspan=2) #占据几个空额, can also be rowspan, one is horizontal, one is vertical ax2.scatter (data[' Tip '],

#-*-coding:utf-8-*-#Data IntegrationImportCSVImportNumPy as NPImportPandas as PDImportMatplotlib.pyplot as Plt#Customer InformationBasicinfo = PD. Dataframe.from_csv ('Datas/basicinfo_train.csv', header=0, sep=',', Index_col=0, Parse_dates=true, Encoding=none, Tupleize_cols=false, infer_datetime_format=False)#Historical Repayment RecordsHistoryinfo = PD. Dataframe.from_csv ('Datas/history_train.csv', header=0, sep=',', Index_col=0, Parse_dates=true, E

(Llen)) Dlen=Randrange (Llen, -) Dom= "'. Join (Choice (LC) forJinch Range(Dlen)) Result_data.append ('%s::%s@%s.%s::%d-%d-%d' %(DTSTR, login, DOM, choice (TLDs), Dtint, Llen, Dlen))#print (result_data)#test reRe_patt= ' ^ (\w{3}).*::(? P forIteminchResult_data:m=Re.match (Re_patt, item)ifM is not None:Print(' * '* -)Print(item)Print("Email:" +M.group (' Email '))Print(' number: ' +M.group (' number '))Tue Jan 15:34:09 1992::[emailprotected]::696584049-7-11email: [email Protected]number:6965840

Objective Pandas is a numpy built with more advanced data structures and tools than the NumPy core is the Ndarray,pandas is also centered around Series and dataframe two core data structures. Series and Dataframe correspond to one-dimensional sequence and two-dimensional table structure respectively. Pandas's conventional approach to importing is as follows: From pandas import series,dataframe impo

This time will be the next issue of SHUANGSE Qiu number forecast, think of a little excitement ah. The code uses the linear regression algorithm, which uses this algorithm to predict the effect, and you can consider using other algorithms to try the results. Before discovering a lot of code is repetitive work, in order to make the code look more elegant, define the function, to call, suddenly tall #!/usr/bin/python #-*-Coding:utf-8-*-#导入需要的包 Import p

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1.1. Pandas Analysis steps Loading data COUNT the date of the access_time. SQL similar to the following: SELECT date_format (access_time, '%H '), COUNT (*) from log GROUP by Date_format (access_time, '%H '); 1.2. Code Cat pd_ng_log_stat.py#!/usr/bin/env python#-*-Coding:utf-8-*-From Ng_line_parser import NglineparserImport Pandas as PDImport socketImport str

specified axisTake and PutRemove a specific element from an arraybroadcasting/BroadcastSpreads along the x-axis and spreads along the y-axis with subtle differencesNp.newaxis () Add new axisAdvanced Ufunc UsageUfunc is the abbreviation for universal function, which is a function that can operate on each element of an array, numpy many of the Ufunc functions are implemented at the C level, so they are computationally fast Np.add.reduce: Add and operate Np.add.accumulate: Similar to

variable (local).-python called namespaces-The following functiondef func(): a = [] for i in range(5): a.append(i)-After Func is called, the empty list A is created first, then 5 elements are added, and a is destroyed when the function exits-If we define a as followsa = []def func(): for i in range(5): a.append(i)-Although you can assign a global variable to a function, those variables must be declared as global variables with

1, remove the label of the page, such as from Import beautifulrsoup predata=beautifulsoup (data,'html.parser'). Get_text ()2. Remove punctuation, etc., with regular expressions.Import RE#表示将data中的除了大小写字母之外的符号换成空格preData=re.sub (R'[^a-za-z]',' , data)3. Lowercase the words in the text and separate the data with a space

element is the index of the item whose index number is smaller than the previous one. So we see that the value of index 2,3 is 1, and the value of index 1 If you want to use the element following the newly inserted index, you need to use the Bfill method The replacement index can be extended from series to dataframe, not only to replace the row index, but also to replace the column index or even replace both Second, delete ① Deleting a series Pandas specificall

Python Object System in-depth analysis, python system in-depth analysis This article analyzes in detail the Python object system. Share it with you for your reference. The details are as follows: Guido uses C language to create Python