pandas isin

Dataframe. drop_duplicates (subset = none, keep = 'first', inplace = false) SubsetTo determine which column duplicate occurs, all columns are considered by default.KeepContains three parametersFirst,Last,False,FirstIt indicates that the first repeat data retrieved is retained and all subsequent data are deleted;LastIndicates that the last retrieved duplicate data is retained and all previously searched duplicate data is deleted,FalseThis means that all searched duplicate data is deleted and non

Data Source acquisition: Https://www.kaggle.com/datasets 1, Look at the some basic stats for the ‘imdb_score’ column: data.imdb_score.describe()Select a column: data[‘movie_title’]Select the first 10 rows of a column: data[‘duration’][:10]Select multiple columns: data[[‘budget’,’gross’]]Select all movies over two hours long: data[data[‘duration’] > 120] data.country = data.country.fillna(‘’)data.duration = data.duration.fillna(data.duration.mean())data = pd.read_csv(‘movie_metadata.csv’, dtype

If the call to the custom top_n takes the agg function, then the reported error will be Illustrates a problem, using the AGG function to call Top_n, it is trying to use top_n for each packet aggregation, but the role of Top_n is a sort, not aggregation, so will definitely error So in this case, you can only use the Apply function, not the AGG function, the function called within the AGG function can only be used to aggregate the grouping. Beginners, personal understanding, if there are errors,

Two data structure series and dataframe.SeriesThe series is the same as a list in Python, with data and index values.Here we create a series object. Data values and indexes for series objects:The index of the list starts at 0, and the series is indexed by default, similar to the list starting with 0. However, you can also customize the index:Indexes can be redefined:Operation elements according to index:Series is also used in the form of dictionaries:Series Auto Alignment: The corresponding valu

="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

For example we have the dataframe like this: SPY AAPL IBM GOOG GLD2017-01-03 222.073914 114.311760 160.947433 786.140015 110.4700012017-01-04 223.395081 114.183815 162.940125 786.900024 110.8600012017-01-05 223.217606 114.764473 162.401047 794.020020 112.5800022017-01-06 224.016220 116.043915 163.200043 806.150024 111.7500002017-01-09 223.276779 117.106812 161.390244 806.650024 112.669998...Now we only we want to get highli

HTML fileBack endExcel_raw_data = Pd.read_excel (Request. Files.get (' Excel_data '))　　Python/django-upload Excel files and use pandas processing

Just urllib2.unquote_plus decoding is not enough, you need to remove the special charactersIllegal_characters_re = Re.compile (R ' [\000-\010]|[ \013-\014]| [\016-\037]|\XEF|\XBF ')Value = Illegal_characters_re.sub ("', Origin_value)Due to the existence of \XEF|\XBF, resulting in string garbled, check this is as Utf-8 BOM existence, need to filter out.Bom:https://en.wikipedia.org/wiki/byte_order_mark#utf-8ASCII characters:http://donsnotes.com/tech/charsets/ascii.htmlThen, it worked for me.PYTHON

1 ImportPandas as PD2DF1 = PD. DataFrame ([[1,2,3],[5,6,7],[3,9,0],[8,0,3]],columns=['X1','X2','X3'])3DF2 = PD. DataFrame ([[1,2],[4,6],[3,9]],columns=['X1','X4'])4 Print(DF1)5 Print(DF2)6DF3 = Pd.merge (df1,df2,how =' Left', on='X1')7 Print(DF3)8DF4 = Pd.merge (df1,df2,how =' Right', on='X1')9 Print(DF4)TenDf5 = Pd.merge (df1,df2,how ='Inner', on='X1') One Print(DF5) ADf6 = Pd.merge (df1,df2,how ='outer', on='X1') - Print(DF6)Getting Started with Python 5 (parameters in merge in

ImportOSImportPandas as PDImportMatplotlib.pyplot as PltdefTest_run (): start_date='2017-01-01'End_data='2017-12-15'dates=Pd.date_range (start_date, End_data)#Create an empty data frameDF=PD. DataFrame (index=dates) Symbols=['SPY','AAPL','IBM','GOOG','GLD'] forSymbolinchsymbols:temp=getadjcloseforsymbol (symbol) DF=df.join (temp, how='Inner') returnDF def Normalize_data (DF): "" " normalize stock prices using the first row of the DATAFR Ame " " " df=df/df.ix[0,:] return DF defGetadj

Close 2017-11-24 260.359985 2017-11-27 260.230011 2017-11-28 262.869995"""if __name__=='__main__': Test_run ()There is a simpy-to-drop the data which index is not present in Dspy:Df1=df1.join (Dspy, how='inner')We can also rename the ' Adj Close ' to prevent conflicts: # Rename the column Dspy=dspy.rename (columns={'Adj Close'SPY'})Load More stocks:ImportPandas as PDdefTest_run (): start_date='2017-11-24'End_data='2017-11-28'dates=Pd.date_range (start_date, End_data)#Create an empty data

This question mainly writes the method of sorting series and dataframe according to index or value Code: #coding =utf-8 Import pandas as PD import numpy as NP #以下实现排序功能. SERIES=PD. Series ([3,4,1,6],index=[' B ', ' A ', ' d ', ' C ']) FRAME=PD. Dataframe ([[2,4,1,5],[3,1,4,5],[5,1,4,2]],columns=[' B ', ' A ', ' d ', ' C '],index=[' one ', ' two ', ' three ']) print the frame print series print ' series is sorted by index: ' print series.sort_index ()

Use Astype as follows: Df[[column]] = Df[[column]].astype (type) 1 1 Type is an int, float, and so on. Example: Import pandas as PD data = PD. Dataframe ([[1, "2"], [2, "2"]]) data.columns = ["One", "two"] print (data) # Current type print ("----\ n modified before type:") print (data.dtypes) # type conversion data [["two"]] = da

The main tasks of data preprocessing are: First, data preprocessing 1. Data cleaning 2. Data integration 3. Data Conversion 4. Data reduction 1. Data cleaningReal-world data is generally incomplete, noisy, and inconsistent. The data cleanup routine attempts to populate the missing values, smoothing the noise and identifying outliers, and correcting inconsistencies in the data. (The data used above) ① Ignore tuples: This is usually done when the class label is missing. This method is not effe

convert to a format that can be found using XPath = Doc.xpath ('//table ') find all the tables in the document and return a list Let's look at the source code of the Web page and find the form that needs to be retrieved The first behavior title of the table, the following behavior data, we define a function to get them separately: def _unpack (Row, kind= ' TD '): ELTs = Row.xpath ('.//%s '%kind) # Get data based on label type return [Val.text_content () For Val in ELTs] # Use

At the time of data processing, especially in the big data contest, often encounter a problem is that multiple forms of merging problems, such as a form has user_id and age two fields, another form has user_id and sex two fields, to merge these two tables into only user_id, Age, sex three fields of the table what to do, the ordinary stitching is not possible, because user_id each row is not the corresponding, like the building blocks of horizontal stitching is certainly not. There is a merge fun

Mysql Index, or the query is slow Note whether the time type will be refreshed after update Design Logic Delete Enable NULL, string numeric operation with function Ifnull (total,0), design-time default value String type (if it contains non-pure numeric data), must be quoted Default value, non-null value must be assigned in advance (TO_SQL) Plus and minus if there is a precision problem, use ABS () > Accuracy error Pandas+mysql

This article brings the content is about Python pandas in-depth understanding (code example), there is a certain reference value, the need for friends can refer to, I hope to help you. First, screening First, create a 6X4 matrix data. Dates = Pd.date_range (' 20180830 ', periods=6) df = PD. DataFrame (Np.arange) reshape ((6,4)), index=dates, columns=[' A ', ' B ', ' C ', ' D ']) print (DF) Print: A B C d2018-08-30 0 1 2 320

Tags: Establish connection copy TOC UTF8 identify Data-nec LDB serviceWrites pandas's dataframe data to the MySQL database + sqlalchemy [Python]View PlainCopyprint? IMPORTNBSP;PANDASNBSP;ASNBSP;PDNBSP;NBSP; fromsqlalchemyimportcreate_engine NBSP;NBSP; # #将数据写入mysql的数据库, However, you need to establish a connection through Sqlalchemy.create_engine, and the character encoding is set to UTF8, otherwise some Latin characters cannot handle ' mysql+mysqldb://roo

1. Python and MySQL connection and operation, directly on the code, simple and direct efficiency:Import MySQLdbTry: Conn= MySQLdb.connect (host='localhost', user='Root', passwd='xxxxx', db='Test', charset='UTF8') cur=conn.cursor () Cur.execute ('CREATE TABLE User (id int,name varchar )') Value= [1,'Jkmiao'] Cur.execute ("INSERT into user values (%s,%s)", value) users= [] forIinchRange -): Users.append ((i,"User"+str (i))) Cur.executemany ("INSERT into user values (%s,%s)", users) Cur.execute