pd dataframe

. loop = Falsedf = pd. concat (chunks, ignore_index = True) byte_sum = df [size]. sum () # traffic statistics top_status_code = pd. dataFrame (df [6]. value_counts () # Status Code statistics top_ip = df [ip]. value_counts (). head (10) # TOP IPtop_referer = df [referer]. value_counts (). head (10) # TOP Referertop_ua = df [ua]. value_counts (). head (10) # TOP U

6.0 5 8.0 Dtype:float64 Shell Create dataframe using a datetime index and a tagged column by passing the NumPy array: Import pandas as PD import numpy as np dates = pd.date_range (' 20170101 ', periods=7) print ( Dates) print ("---" *16) = PD. Dataframe(np.random.randn (7,4), index=dates, columns=list (' ABCD

= 5status_code = 6 size = 7 referer = 8ua = 9 # read the log into DataFramereader = pd. read_table (log_file, sep = '', names = [I for I in range (10)], iterator = True) loop = TruechunkSize = 10000000 chunks = [] while loop: try: chunk = reader. get_chunk (chunkSize) chunks. append (chunk) failed t StopIteration: # Iteration is stopped. loop = Falsedf = pd. concat (chunks, ignore_index = True) byte_sum =

2018.03.26 common Python-Pandas string methods, Import numpy as npImport pandas as pd1 # common string method-strip 2 s = pd. series (['jack', 'jill', 'jease ', 'feank']) 3 df = pd. dataFrame (np. random. randn (3, 2), columns = ['column A', 'column B '], index = range (3) 4 print (s) 5 print (df. columns) 6 7 print ('----') 8 print (s. str. lstrip (). values) #

Getting started with Python for data analysis--pandas Based on the NumPy established from pandas importSeries,DataFrame,import pandas as pd One or two kinds of data structure 1. Series A python-like dictionary with indexes and values Create a series#不指定索引，默认创建0-NIn [54]: obj = Series([1,2,3,4,5])In [55]: objOut[55]:0 11 22 33 44 5dtype: int64#指定索引In

of the Python program, such as: CSV, JSON, line-bound JSON, and remote versions of all of the above HDF5 (standard format and pandas format are available), Bcolz, SAS, SQL database (SQLAlchemy supported), Mongo An into project can efficiently migrate data between any two formats in the data format, using a pair-switched network (intuitive explanation at the bottom of the article). How to use it The into function has two parameters: source and target. It converts data from source to target.

10-lesson from Dataframe to Excel from Excel to Dataframe from Dataframe to JSON, from JSON to Dataframe Import pandas as PD import sys Print (' Python version ' + sys.version) print (' Pandas version ' + pd.__version__) Pyt

pandas as PD def process (): s = Series ([1, 4, ' www ', ' tt ']) print s print s.index print s.values s2 = Series ([' Ch Ao ', ' Man ', ' index=['], "name", ' Sex ', ' age ']) print s2 print s2[' name ' s2[' name '] = ' chen ' Print s2 SD = {' score ': 329, ' age ':} s3 = Series (SD) print s3 S33 = Series ({' Score ': 329, ' age ':}) Print S33 S4 = Series (SD, index=[' Java ', ' score ', ' age ']) print S4 print Pd.isnull (S4) Print s4.isn

# Coding:utf-8__author__ = ' Weekyin 'Import NumPy as NPImport Pandas as PDDatas = Pd.date_range (' 20140729 ', periods=6)# first create a time index, the so-called index is the ID of each row of data, you can identify the unique value of each rowPrint Datas# for a quick start, let's look at how to create a 6x4 data: The RANDN function creates a random number, the parameter represents the number of rows and columns, and dates is the index column created in the previous stepDF =

Importprint_functionImportPandas as PD fromSklearn.clusterImportKmeans#Import K-mean clustering algorithmdatafile='.. /data/data.xls' #data files for clusteringProcessedfile ='.. /tmp/data_processed.xls' #file after data processingTypelabel ={u'syndrome type coefficient of liver-qi stagnation':'A', u'coefficient of accumulation syndrome of heat toxicity':'B', u'coefficient of offset syndrome of flush-type':'C', u'The coefficient of Qi and blood defic

import NumPy as NPImport Pandas as PD1 #string Common methods-strip2s = PD. Series (['Jack','Jill','Jease','Feank'])3DF = PD. DataFrame (Np.random.randn (3,2), columns=['Column A','Column B'],index=range (3))4 Print(s)5 Print(df.columns)6 7 Print('----')8 Print(S.str.lstrip (). Values)#Remove the left space9 Print(S.str.rstrip (). Values)#Remove the space on the

median.Df. var () returns the varianceDf. std () evaluate standard deviationDf. mad () calculates the mean absolute spread based on the average value.Df. cumsum () calculates the sumSr1.corr (sr2) returns the correlation coefficientDf. cov () returns the covariance matrix.Df1.0000with (df2) Correlation Coefficient Pd. cut (array1, bins) for interval distribution of One-Dimensional DataPd. qcut (array1, 4) divides intervals by specified quantiles, an

Pandas mainly has 4 of the time-related types. Timestamp, Period, Datetimeindex,periodindex.ImportPandas as PDImportNumPy as NP##TimestampPd. Timestamp ('9/1/2016 10:05am')#output:timestamp (' 2016-09-01 10:05:00 ')##PeriodPd. Period ('1/2016')#output:period (' 2016-01 ', ' M ')Pd. Period ('3/5/2016')#output:period (' 2016-03-05 ', ' D ')##DatetimeindexT1 = PD. Series (List ('ABC'), [

) plt.show () Exe Rciseone () #-*-Coding:utf-8-*-"" "__author__ = ' ljyn4180 '" "" Import NumPy as NP import pandas as PD import Matplotlib.pyplot a S PLT Import mpl_toolkits.mplot3d.axes3d as Axes3d def costfunction (Matrixx, Matrixy, Matrixtheta): Inner = Np.power (((Matrixx * matrixtheta.t)-Matrixy), 2) return Np.sum (Inner)/(2 * len (Matrixx)) def gradientdescent (Matrixx, MA Trixy, Matrixtheta, Falpha, nitercounts): Matrixthetatemp = Np.ma

Python crawler ', ' Data analysis ', ' machine learning ' #这里支持度设置为0.3 While Len (column) > 1: column = connect_string (column, ms) #连接 SF = Lambda I:d[i].prod (Axis=1, NUMERIC_ONLY=TR UE) #定义连乘函数 d_2 = PD. Dataframe (Map (SF, column), Index=[ms.join (i) for I in column]). T #创建连接数据 >>>column [[' Java ', ' Python crawler '], [' Java ', ' data analysis '], [' Java ', ' machine learning '

Data conversionDelete duplicate elements　　The duplicated () function of the Dataframe object can be used to detect duplicate rows and return a series object with the Boolean type. Each element pairsshould be a row, if the row repeats with other rows (that is, the row is not the first occurrence), the element is true, and if it is not repeated with the preceding, the metaThe vegetarian is false.A Series object that returns an element as a Boolean is of

Original English: 11-lesson Reads data from multiple Excel files and merges the data together in a dataframe. Import pandas as PD import matplotlib import OS import sys %matplotlib inline Print (' Python version ' + sys.version) print (' Pandas version ' + pd.__version__) print (' matplotlib version ' + Mat PLOTLIB.__VERSION__) Python version 3.6.1 | Packaged b

should be used like this: First, you need to put the code in a separate configuration file, such as config. py. Then, import the configuration file where you need it. 1 from config import con_analyze 2 3 4 class AnalyzeData: 5 def _ init _ (self): 6 # initialization here, You can include a parameter: database, the default is myanalyze 7 self. conn = con_analyze () 8 # self. conn2 = con_analyze ("myanalyze_2") 9 10 def get_data (self, SQL): 11 # Save the SQL query result to df 12 df = self. conn

In addition to the series, dataframe these two commonly used data structures in the Pandas library, there is also a panel data structure that can typically be created with a dictionary of Dataframe objects or a three-dimensional array to create a Panel object. 1 # 2 3 created on Sat Mar 18:01:05 4 5 @author: Jeremy 6 7 import NumPy as NP 8 from Pandas import Series,

The main data were three kinds of preprocessing: 1. Interval Scaling reading data, data processing, storing data Import pandas as PD import NumPy as NP from Sklearn import preprocessing import matplotlib.pyplot as PLT p lt.rcparams[' Font.sans-serif '] =[' Simhei '] #用来正常显示中文标签 plt.rcparams[' Axes.unicode_minus '] =false #用来正常显示负号 filename = ' Hits persecond_t20m_130.csv ' data_f = pd.read_csv (filename) #二维