pandas indexing

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# 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 creat

()Print(c isA#False (C and a point to memory addresses are different)#copy A, assign a value to C#if it is c=a, then C and a are the same (point to the same address)#Print (c is a) in the word, it prints truec[1,2] = 100Print(a)" "[ [1 2 3] [4 5] [7 8 9]]" "#here we find that C has been modified, so a has also been modified.#C and a have different addresses but share a set of dataD=a.copy ()Print(d isA#falsed[1,3] = 100#There's no change here .Print(a)Read TXT file:Import NumPy # The first para

Original addressThe coding of discrete features is divided into two situations:1, the value of discrete features do not have the meaning of the size, such as Color:[red,blue], then use one-hot encoding2, discrete characteristics of the value of the size of the meaning, such as SIZE:[X,XL,XXL], then use the value of the map {X:1,xl:2,xxl:3}It is convenient to use pandas to one-hot encoding of discrete features Import

Import NumPy as NP Import Pandas as PD DATA=PD. Dataframe (Np.arange (6). Reshape ((3,2)), INDEX=PD. Index ([' A ', ' B ', ' C '],name= ' state '), COLUMNS=PD. Index ([' I ', ' II '],name= ' number ')] Data Number I II State A 0 1 B 2 3 C 4 5 Result=data.unstack () Result Number State I a 0 B 2 C 4 II a 1 B 3 C 5 Type (Result) #pandas. Core.series.Ser

1 concat The Concat function is a method underneath the pandas that allows for a simple fusion of data based on different axes. Pd.concat (Objs, axis=0, join= ' outer ', Join_axes=none, Ignore_index=false, Keys=none, Levels=none, Names=None, Verify_integrity=false)1 2 1 2 1 2 Parameter descriptionObjs:series,dataframe or a sequence of panel compositions lsitAxis: Axis that needs to merge links, 0 is row, 1 is columnJoin: Connecting the way i

dateframe Modify column names in Pandas The data are as follows: >>>import pandas as PD >>>a = PD. Dataframe ({' A ': [1,2,3], ' B ': [4,5,6], ' C ': [7,8,9]}) >>> a a B C 0 1 4 7 1 2 5 8 2 3 6 91 2 3 4 5 6 7 1 2 3 4 5 6-7 method One: Methods of violence >>>a.columns = [' A ', ' B ', ' C '] >>>a a b c 0 1 4 7 1 2 5 8 2 3 6 91 2 3 4 5 6 1 2 3 4 5-6 But the disadvantage is that you

The is very simple to use when data manipulation is done through the Pandas library, and then a brief instance is written to the CSV file: In [1]: Import pandas as PD in [2]: data = {' Row1 ': [1,2,3, ' Biubiu '], ' row2 ': [3,1,3, ' Kaka ']} in [3]: Data out[3]: {' row1 ': [1, 2, 3, ' Biubiu '], ' row2 ': [3, 1, 3, ' Kaka ']} in [4]: DATA_DF = PD. Dataframe (data) in [5]: DATA_DF out[5]: row1 row2 0

Objective Pandas is a numpy built with more advanced data structures and tools than the NumPy core is the Ndarray,pandas is also centered around Series and dataframe two core data structures. Series and Dataframe correspond to one-dimensional sequence and two-dimensional table structure respectively. Pandas's conventional approach to importing is as follows: From

Operating system: Windowspython:3.5Welcome to join the Learning Exchange QQ Group: 657341423 The previous section describes the library of data analysis and mining needs, the most important of which is pandas,matplotlib.Pandas: Mainly on data analysis, calculation and statistics, such as the average, square bad.Matplotlib: The main combination of pandas to generate images. Both are often used in combination

merging and splitting of arrays in numpy and pandas Merging in NumPy In NumPy, you can combine two arrays on both the vertical and horizontal axes by concatenate, specifying parameters axis=0 or Axis=1. Import NumPy as NP import pandas as PD Arr1=np.ones (3,5) arr1 out[5]: Array ([[1., 1., 1., 1., 1.], [1., 1 ., 1., 1., 1.], [1., 1., 1., 1., 1.]] Arr2=np.random.randn. Reshape (Arr1.shape) arr2 out[8]: A

, 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

Time series data Statistics-sliding window window functionsimport pandas as pdimport numpy as npser_obj = pd.Series(np.random.randn(1000), index=pd.date_range('20180101', periods=1000))ser_obj = ser_obj.cumsum()print(ser_obj.head())2018-01-01 0.7973342018-01-02 0.4512862018-01-03 1.3291332018-01-04 0.4165772018-01-05 0.610993Freq: D, dtype: float64r_obj = ser_obj.rolling(window=5)r_obj2 = ser_obj.rolling(window=5, ce

Below for you to share a pandas implementation will repeat the table to weight, and re-converted to a table method, has a good reference value, I hope to be helpful to everyone. Come and see it together. Dataframe and set are often used when processing data in Python. Train=pd.read_csv (' xxx.csv ') #读取文件 train=train[' item_id ') #选择要去重的列 Train=set (train) #去重 DATA=PD. DataFrame (List (train), columns=[' item_id ']) #因为set是无序的, must go through li

, ' Www.bing.com ': 777, ' www.aaa.com ': 1113101, ' www.ccc.net.cn ': 922, ' www.zhanimei.ga ': 29847, ' www.zhanimei.ml ': 40155, ' Www.zhasini.ml ': 373436} I only took the first few, and organized it into a dictionary. Start drawing From pandas import Series,dataframeimport Matplotlib.pyplot as Pltplt.figure (figsize= (8,6), dpi=80) ts = Series (d) Ts.plot (kind= ' Barh ') plt.savefig ('/var/www/jastme/static/images/log.png ') HTML to write the I

The following for you to share a pandas implementation of the selection of a specific index of the row, has a good reference value, I hope to be helpful to everyone. Come and see it together. As shown below: >>> Import numpy as np>>> import pandas as pd>>> Index=np.array ([2,4,6,8,10]) >>> Data=np.array ([3,5,7,9,11]) >>> DATA=PD. DataFrame ({' num ':d ata},index=index) >>> print (data) num2 910 11

This article mainly introduces the method of pandas to filter data according to the combination condition of several columns, has certain reference value, now share to everybody, the need friend can refer to Or do you speak with a picture? A file: For example, I would like to filter out "design Wells", "put into production Wells", "current well" three columns of data are 11 data, the results are as follows: Of course, the filter conditions here can

The following for everyone to share a pandas GroupBy group to take the first few lines of the record method, with a good reference value, I hope to be helpful to everyone. Come and see it together. Directly on the example. Import Pandas as PD df = PD. DataFrame ({' Class ': [' a ', ' a ', ' B ', ' B ', ' A ', ' a ', ' B ', ' C ', ' C '], ' score ': [3,5,6,7,8,9,10,11,14]}) Df: class

Below for you to share a pandas multilevel grouping implementation of the method of sorting, with a good reference value, I hope to be helpful to everyone. Come and see it together. Pandas have groupby grouping functions and sort_values sort functions, but how do you sort the dataframe after grouping them? in []: DF = PD. DataFrame ((Random.randint), Random.choice ([' Tech ', ' art ', ' Office '), '%dk

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 environment is Windows bit+python2.7.11.A Install