pandas slice

minus minimum for each columnThe Apply function enters a sequenceValue_counts () View the number of occurrences of an element with. Mode () View the most frequently occurring elementsCreate a random sequence firstCall Value_counts ()Call. Mode () to see the most frequently occurring elementsData mergeCreate an array of 10*4 first(1) Call the concat () function to merge the array (Concat accepts an array, which is the array to be merged)See if the merged array is equal to the original arrayOr(2)

The most by a friend set up a part-time operation of the company, but the need for some part-time staff pay, but due to a part-time wage between the 40~60, so the company adopted the principle is more than 200 to carry out, this rule is equivalent to drop the driver, the withdrawal needs more than 200, Then the problem came, in order to better let a large number of part-time staff can, clearly understand the time period in which they earn a lot of money, this time extended a problem, we need to

the unique value of A, the number of occurrences (a, b) of the unique value of statistics = (1,3) c appears 1 times (A, B) = (2,4) appears 3 times - the Print(Pd.crosstab (df['A'],df['B'],normalize=true))#display in a frequency-based manner - Print('--------') - Print(Pd.crosstab (df['A'],df['B'],values=df['C'],aggfunc=np.sum))#values: A value array based on a factor aggregation - #Aggfunc: If the values array is not passed, the frequency table is computed, and if the array is passed, the calc

Ming 6.0 - Name:price, Dtype:float64 -Zhang San 1.2 theReese 1.0 -Harry 2.3 -Chen Jiu 5.0 -Xiao Ming 6.0 +Name:price, Dtype:float64　　In general, we often need to value by column, then Dataframe provides loc and Iloc for everyone to choose from, but the difference is between the two.1 Print(frame2)2 Print(frame2.loc['Harry'])#Loc can use the index of the string type, whereas the Iloc can only be of type int3 Print(frame0.iloc[2])4 out[2]: 5 Color Object Price6Zhang San Blue ball 1.27Reese Green

1. Create a dataframe from a dictionary>>>ImportPandas as PD>>> Dict1 = {'col1': [1,2,5,7],'col2':['a','b','C','D']}>>> DF =PD. DataFrame (Dict1)>>>DF col1 COL201a1 2b2 5C3 7 D2. Create Dataframe from multiple lists (convert the list to a dictionary, then convert the dictionary to dataframe)>>> lista = [1,2,5,7]>>> LISTB = ['a','b','C','D']>>> df = PD. DataFrame ({'col1': Lista,'col2': Listb})>>>DF col1 COL201a1 2b2 5C3 7 DPython Pandas Dataframe oper

Sometimes we can rank and sort series and dataframe based on the size of the index or the size of the value. A, sorting Pandas provides a Sort_index method that sorts A, series sort 1, sorted by index based on the index of rows or columns in the order of the dictionary. #定义一个Series s = Series ([1,2,3],index=["A", "C", "B"]) #对Series的索引进行排序, the default is ascending print (S.sort_index ()) ' a 1 b 3 C 2 '

Official documents: Pandas. Dataframe.unstack¶Dataframe. Unstack (Level=-1, fill_value=none) [source]¶ Pivot A level of the (necessarily hierarchical) index labels, returning a DATAFRAME has a new level of column labels WH OSE Inner-most level consists of the pivoted index labels. If The index is not a multiindex, the output would be a Series (the analogue's stack when the columns are not a multiindex (when there is only one row index, the result gene

, 0.69033553], [ -0.91894216, -0.70341454, -0.17903858, -0.08491163, 2.08802511], [ -0.3333518, 1.56342694, 0.48037342, 0.92744459,-0.49513354]] Arr[np.argsort (arr[:,0]),:] # from the No. 0 column from small to large arrangement Array ([[ -0.91894216, 2.08802511,-0.70341454,-0.08491163,-0.17903858], [ -0.3333518, -0.49513354, 1.56342694, 0.92744459, 0.48037342], [0.06508931, 0.56513883, 0.62546144,-1.28835261,-2.08906088], [1.5425056, 0.69033553, 1.60385421,-1.52568607,-

date belongs to a leap year Import pandas as PD Df=pd.read_excel ("C:/users/administrator/desktop/new Microsoft Excel worksheet. xlsx") #读取工作表 DF [Property],df[' Description ']=df[' property Description '].str.split ("", n=1). str# divide by first space Df.drop ("Property Description ", axis=1,inplace=true) #删除原有的列 df.to_csv (" C:/users/administrator/desktop/new Microsoft Excel Worksheet. csv ", Index=false) #保存为csv, and delete the index Th

) pd.read_sql_table (table_name, con, Schema=none, Index_col=none, Coerce_float=true, Parse_dates=none, columns= None, Chunksize=none) For example: data = pd.read_sql_table (table_name = ' t_line ', con = engine,parse_dates = ' time ', Index_col = ' time ', columns = [' A ', ' B ', ' C ']) 3: Read database (via SQL statement or table name) See me through the SQL statement another article: http://www.cnblogs.com/cymwill/articles/7576600.html pd.read_sql (sql, con, index_col=none, Coerce_float=t

Label:Read the contents of the table, as in the following example: ImportMySQLdbTry: Conn= MySQLdb.connect (host='127.0.0.1', user='Root', passwd='Root', db='MyDB', port=3306) DF= Pd.read_sql ('select * from test;', con=conn) Conn.close ()Print "Finish Load DB" exceptmysqldb.error,e:PrintE.ARGS[1] Write the data to the table, as in the following example DF = PD. DataFrame ([[1,'XXX'],[2,'yyy']],columns=list ('AB')) Try: Conn= MySQLdb.connect (host='127.0.0.1', user='Root', passwd='Root', db='My

indexes and slices for numpyThe contents of the Ndarray object can be viewed and modified through indexing and tiling.Index: An element index in an Ndarray object based on 0Slice: A description of a region of a fragment in an arrayThe slices of an array can also be understood as a partial view of the original array, all pointing to the original array in memory, so the changes on the slices are reflected directly on the original array, unlike the list copy . Example code for an indexed tile demon

It is found that most people use Array. prototype. slice. call (argments, 0) and never understand what this sentence is. I reviewed the slice () method yesterday, and then referred to Function. call (thisArg [, arg1 [, arg2 [,...]), I still don't know (my mind is slow: | ). Writing front In js, we often see Array. prototype. slice. call (arguments, 0), of course

This is a creation in Article, where the information may have evolved or changed. The slice type in the Go language can be interpreted as a descriptor of the array type, containing three factors: Pointer to underlying array Slice the number of elements of the underlying array currently used, that is, the length The maximum length of the underlying array, which is the capacity So when we defin

The meaning and relationship of nal, slice and frame in H.NAL Nal_unit_type 1 (encoding strip for non-IDR images), 2 (coded stripe data split block a), 3 (coded stripe data split block B), 4 (coded stripe data block C), 5 (encoded stripe of IDR image) typesAndThree coding modes for slice species: I_slice, P_slice, B_sliceThere are 3 types of frame: what is the mapping between I-frame, P-frame, and B-frame?F

1-Slice initialization 1Func Printslice (S []int) {2Fmt. Printf ("len=%d cap=%d underlying array:%p,%v\n", Len (s), Cap (s), S, s)3 }4 5 func sliceinit () {6 7 varS1 []int//declares S1, and does not initialize, at this time S1 is nil slice, no underlying array is assigned8 ifS1 = =Nil {9Fmt. Println ("S1 is nil")Ten } One AS2: = []int{} - ifS2 = =Nil { -Fmt. Println ("S2 is nil") the }

Let me briefly introduce the two commonly used data structures, series and daraframe in Python, which are defined by the Pandas module. The series is similar to dict in Python, but is structured, and dataframe is similar to a table in a database.1.pandas basic data Structure-pandas. Seriespandas. DataFramethe second method of defining Dataframe cannot set index m

About Python data analysis in the Pandas module in the output, the middle of each line will have ellipses appear, and lines and lines in the middle of the ellipsis .... Problem, most of the other sites (Baidu) are written blindly, is simply copy paste the previous version, you want to know the answer to other questions you have to read the official documents.1 #!/usr/bin/python2 #-*-coding:utf-8-*-3 ImportNumPy as NP4 ImportPandas as PD5 ImportMySQLdb

about installing the configuration Numpy,scipy,matplotlibm,pandas and Sklearn under Ubuntu The most recent learning machine in Python is the need to configure related components. Also checked on the Internet some, summed up a bit. By the way, if there is any mistake, please point out, thank you.Recommended links to configuration and corresponding installation packages in Windows environment you can take a look. My system environment is ubuntu14.04lts

#!/usr/bin/env python #-*-coding:utf-8-*-# @Time: 4/14/18 4:16 PM # @Author: Aries # @Site: # @File: t imeseries_demo.py # @Software: Pycharm ' Pandas time Series reference: https://blog.csdn.net/ly_ysys629/article/details/73822716 https://blog.csdn.net/pipisorry/article/details/52209377 official document:http://pandas.pydata.org/pandas-docs/ stable/http://pandas.pydata.org/