python pandas tutorial

From OPENPYXL import load_workbook import pandas as PDdata = Pd.read_excel (' test1.xlsx ', sheetname=0) # col_data = List (data.ix[:, 5]) # Gets the fifth column that starts outside the header Row_data = List (data.ix [5,:]) # Gets the fifth row of data except the header starting with writer = PD. Excelwriter (' test2.xlsx ', engine= ' OPENPYXL ') book = Load_workbook (' test2.xlsx ') writer.book = Book result = PD. DataFrame (Row_data) result.to_exc

7.0 8 df 0 1 2 0 NaN NaN 2 1 NaN NaN 5 2 6.0 7.0 8 df.fillna({0:111:22}, inplace=True) 0 1 2 0 11.0 22.0 2 1 11.0 22.0 5 2 6.0 7.0 8 df 0 1 2

. Timestamp (' 20140729 '), ' B ': PD. Series (1, Index=list (range (4))),})Print DF2# You can use Dtypes to see the data formats for each rowPrint Df2.dtypes# then look at how to view the data in the data frame and see all the dataPrint DF# Use Head to see the first few rows of data (default is the first 5 rows), but you can specify the first few linesPrint Df.head ()# View the first three rows of dataPrint Df.head (3)# Use Tail to view the following 2 rows of dataPrint Df.tail (2)# View the in

SummaryThe use of Python for data analysis, you need to install some common tools, such as numpy,pandas,scipy, etc., during the installation process, often encountered some installation details problems, such as version mismatch, need to rely on the package is not installed properly, etc. This article summarizes the next few necessary installation package installation steps, hoping to help readers, the envi

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Time series PV-GMV Double axis line chartImport NumPy as Npimport pandas as Pdimport matplotlib.pyplot as Pltn = 12date_series = Pd.date_range (start= ' 2018-01-01 ', Periods=n, freq= "D") data = { ' PV ': [10000, 12000, 13000, 11000, 9000, 16000, 10000, 12000, 13000, 11000, 9000, 16000], ' GMV ': [+-------------- DataFrame (data, index=date_series) ax = df.plot ( secondary_y=[' GMV '), x_compat=true, grid=true) ax.set_ Title ("PV-GMV")

Using XLRD to read ExcelFilter 0 columns with a value greater than 99% and removeImport XlrdWorkbook=xlrd.open_workbook (R "123.xlsx")Table = Workbook.sheet_by_name (' Sheet1 ')Nrows=table.nrowsNcols=table.ncolsDel_col=[]For j in Range (Ncols):sum = 0For Ai in table.col_values (j):if ai = = 0.0:Sum+=1if float (sum)/nrows>=0.99:Del_col.append (j)print Del_col Using Pandas to read ExcelFilter 0 columns with a value greater than

The processing of the data is pandas, but it has not been learned and does not know whether there is a method call that is directly normalized to a column. Himself dealing things down. The feeling is still more troublesome.After reading to the array using pandas, I want to have the ' monthlyincome ' column normalized, and the chestnuts on the web are normalized to the entire dataframe, because some of my da

, 164.000000f, 159.000000f, 157.000000f, 145.000000f, 135.000000f, 120.000000f, 104.000000f, 88.000000f, 77.000000f, Surface Chart Scripts # -*- coding: utf-8 -*-from matplotlib import pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dfrom pandas import DataFramedef draw(x, y, z):‘‘‘采用matplolib绘制曲面图:param x: x轴坐标数组:param y: y轴坐标数组:param z: z轴坐标数组:return:‘‘‘X = xY = yZ = zfig = plt.figure()ax = fig.add_subplot(111, projection=‘3d

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:

ordered data such as time series, it may be necessary to do some interpolation when re-indexing, the method option can achieve this purpose:For ordered data such as time series, it may be necessary to do some interpolation when re-indexing, the method option can achieve this purpose: Method Parameter Introduction Parameters Description Ffill or pad Forward padding Bfill or Backfill Back to fill

Python uses pandas and xlrd to read excel files, feature filtering columns, and pandasxlrd Use xlrd to read excelFilter and delete columns with 0 values over 99%.Import xlrdWorkbook = xlrd. open_workbook (R "123.xlsx ")Table = workbook. sheet_by_name ('Sheet1 ')Nrows = table. nrowsNcols = table. ncolsDel_col = []For j in range (ncols ):Sum = 0For ai in table. col_values (j ):If ai = 0.0:Sum + = 1If

:00:00 ')2. Time interval# #TimedeltasPD. Timestamp ('9/3/2016')-pd. Timestamp ('9/1/2016')# timedelta (' 2 days 00:00:00 ')PD. Timestamp ('9/2/2016 8:10AM') + PD. Timedelta ('12D 3H')# Timestamp (' 2016-09-14 11:10:00 ') 3. Time in the DataframeDates = Pd.date_range ('10-01-2016', Periods=9, freq='2w-sun') Dates"""Datetimeindex ([' 2016-10-02 ', ' 2016-10-16 ', ' 2016-10-30 ', ' 2016-11-13 ', ' 2016-11-27 ', ' 2016-12-11 ', ' 2016 -12-25 ', ' 2017-01-08 ', ' 2017-01-22 '], dtype= ' datetime64

Delete one or more columns of Pandas Dataframe:method One : Direct del df[' Column-name ']method Two : Using the Drop method, there are three types of equivalent expressions:1. df= df.drop (' column_name ', 1);2. Df.drop (' column_name ', Axis=1, Inplace=true)3. Df.drop ([df.columns[[0,1, 3]], axis=1,inplace=true) # Note:zero indexedNote : Usually there is a inplace optional parameter that modifies the original array and returns a new array. If set to

I believe many people like me in the process of learning Python,pandas data selection and modification has a great deal of confusion (perhaps by the Matlab) impact ... To this day finally completely figure out ... Let's start with a data box manually. Import NumPy as NP import pandas as PD DF = PD. Dataframe (Np.arange (0,60,2). Reshape (10,3), columns=list (' a

label as a numpy array of Python objects Int64index Special index for integers Multiindex A hierarchical Index object that represents a multi-level index on a single axis. Can be seen as an array of tuples Datetimeindex Memory nanosecond timestamp (denoted by NumPy's Datetime64 type) Periodindex Special index for period data (time interval) 2.2.d.1 Primary Inde

Hierarchical Indexes Hierarchical indexing means you can have multiple indexes on an array, for example: a bit like a merged cell in Excel, right?Select a subset of the data based on the index to select a subset of the data from the other layer:Select data in the same way as the index in the layer:Multi-index series conversion to Dataframe hierarchical indexes play an important role in data reshaping and grouping, for example, the hierarchical index data above can be converted to a dataframe:For

Use Python for data analysis _ Pandas _ basic _ 2, _ pandas_2Reindex method of Series reindex In [15]: obj = Series([3,2,5,7,6,9,0,1,4,8],index=['a','b','c','d','e','f','g', ...: 'h','i','j'])In [16]: obj1 = obj.reindex(['a','b','c','d','e','f','g','h','i','j','k'])In [17]: obj1Out[17]:a 3.0b 2.0c 5.0d 7.0e 6.0f 9.0g 0.0h 1.0i 4.0j 8.0k NaNdtype: float64 If the current va

Using Python for data analysis (13) pandas basics: Data remodeling/axial rotation, pythonpandas Remodeling DefinitionRemodeling refers to re-arranging data, also called axial rotation.DataFrame provides two methods: Stack: rotate the column of data into rows. Unstack: "Rotate" data rows as columns. For example: Process stack formatThe stack format is also called the long format. Generally, the data

="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