pd iloc

). Strftime ('%y-%m-%d ') P2=get_price (Index[each] , start_date=p1_30d, end_date=p1_30d, frequency= ' daily ', fields= ' close ') if not p2.empty: Pass ELSE:DATE_STAMP2 = Datetime.strptime (p1_30d, '%y-%m-%d ') unix2=time.mktime (date_stamp 2.timetuple ()) Unix2_weekend=unix2-unix_weekend P2_weekend=datetime.fromtimestamp (unix2_weekend). Str Ftime ('%y-%m-%d ') P2=get_price (Index[each], start_date=p2_weekend, end_date=p2_weekend, frequency= ' Daily ', fields= ' close ')

With LIGHTGBM and Xgboost respectively made the kaggle digit recognizer, try to use GRIDSEARCHCV tune the next parameter, mainly to Max_depth, Learning_rate, N_ Estimates and other parameters to debug, finally in 0.9747. Capacity is limited, and next we don't know how to further adjust the parameters. In addition, the Xgboost GRIDSEARCHCV will not be used, if there is a great God will, please inform. Paste the LIGHTGBM code: #!/usr/bin/python Import NumPy as NP import pandas as

from:76713387How to iterate through rows in a DataFrame in pandas-dataframe by row iterationHttps://stackoverflow.com/questions/16476924/how-to-iterate-over-rows-in-a-dataframe-in-pandasHttp://stackoverflow.com/questions/7837722/what-is-the-most-efficient-way-to-loop-through-dataframes-with-pandasWhen it comes to manipulating dataframe, we inevitably need to view or manipulate the data row by line, so what's the efficient and fast way to do it?Index ordinalimport pandas as pdinp = [{‘c1‘:10, ‘c2

use anonymous functions5 column names1 Df.columns2Df.columns = ['a','b','C','e','D','F']# Renaming3Df.rename (columns = {'A':'AA','B':'BB','C':'cc','D':'DD','E':'ee','F':'FF'}, Inplace=True)4Df.rename (columns=LambdaX:x[1:].upper (), inplace=true)#You can also use the function inplace parameter to replace the original variable, the deep copy6 Dummy Variable Dummy variables1 PD. Series (['a|b'a|c']). Str.get_dummies ()7 Pure DF Matrix, i.e. does not c

the data has been very clean, where the main task of data preprocessing is to discretization of each attribute, and to cluster each attribute into 4 classes. This is done to accommodate the needs of the algorithm because the association rule algorithm cannot handle continuous data The key to clustering each attribute into the 4 class is to find the right dividing point. The dividing point is determined by clustering algorithm to find the cluster center of each attribute, taking the average valu

Organize Pandas Operations This article original, reproduced please identify the source: http://www.cnblogs.com/xiaoxuebiye/p/7223774.html Import Data: Pd.read_csv (filename): Import data from CSV file pd.read_table (filename): Import data from a delimited text file pd.read_excel (filename) : Importing data from an Excel file pd.read_sql (query, Connection_object): Importing data from SQL Tables/Libraries Pd.read_json (json_string) : Import data from JSON-formatted string pd.read_html (URL): P

to a DataFrame: >>> import pandas as pd>>> from pandas import Series,DataFrame>>> rnames = ['user_id','movie_id','rating','timestamp']>>> ratings = pd.read_table(r'ratings.dat',sep='::',header=None,names=rnames)>>> ratings[:3] user_id movie_id rating timestamp0 1 1193 5 9783007601 1 661 3 9783021092 1 914 3 978301968 [3 rows x 4 columns] The ratings table only uses the user_id, movie_id, and rating columns. Therefore, we can retrieve these t

', ' 110 ') Replace Data preprocessing Sort the data Df.sort_values (by=[' The number of messages sent by the customer on the Day ']) Sort PivotTable report in data grouping --excel* * Group Customer chat Records #如果price列的值 >3000,group column shows high, otherwise show low df[' group ' = Np.where (df[' customer sends messages on the day '] > 5, ' High ', ' low ') DF Group grouping to meet multiple criteria # >24 in sign column with broker-level A1 and broker response length shown as 1 df

", Key );Dl_a = delete_dnode (dl_a, key );Printf ("after deleted the list is:/N ");Disp_dlinklist (dl_a ); Printf ("/ninput the POs and the key to insert:/N ");Scanf ("% d", Pos, Key );Dl_a = insert_dnode (dl_a, POs, key );Printf ("after inserting the list is:/N ");Disp_dlinklist (dl_a ); Printf ("/nafter reverse the list is:/N ");Dl_a = reverse_dlinklist (dl_a );Disp_dlinklist (dl_a ); Printf ("/nafter sorted the list is:/N ");Dl_a = sort_dlinklist (dl_a );Disp_dlinklist (dl_a ); Printf ("/n

} converts selected columns using the function func, which usually indicates the number of columns (avoid converting to int) Dfjs = pd. read_json ('file. json') can be passed in a json stringDfex = pd.read_excel('file.xls ', sheetname = [0, 1 ..]) read multiple sheet pages and return the dictionary of multiple df 3. data preprocessing Df. duplicated () returns whether the row is a duplicate row of the previous row.Df. drop_duplicates () deletes dup

Original English: 04-lesson In this lesson, we will revert to some basic concepts. We'll use a smaller dataset so you can easily understand the concepts I'm trying to explain. We will add columns, delete columns, and slice the data (slicing) operations in different ways. enjoy! # Import required Libraries import pandas as PD import sys Print (' Python version ' + sys.version) print (' Pandas version: ' + pd