Python数据分析第十二课：单变量、双变量及多变量分析图

2023-11-04 16:25| 来源: 网络整理| 查看: 265

一、单变量分析绘图

什么是单变量分析？

单变量其实就是我们通常接触到的数据集中的一列数据。

单变量分析是数据分析中最简单的形式，其中被分析的数据只包含一个变量。因为它是一个单一的变量，它不处理原因或关系

单变量分析的主要目的是描述数据并找出其中存在的模式，也就是“用最简单的概括形式反映出大量数据资料所容纳的基本信息”。

本节我们研究的是连续数值型数据的分布。

那么什么样的数据是连续数值型数据呢？什么样的数据是离散型数据呢？

连续型数据一般应用在计算机领域，在数据挖掘、数据分类时会遇到此类数据，因其数据不是单独的整十整百的数字，包含若干位小数且取值密集，故称为连续型数据，例如，身高、体重、年龄等都是连续变量。由记录不同类别个体的数目所得到的数据，称为离散型数据。例如，某一类别动物的头数，具有某一特征的种子粒数，血液中不同的细胞数目等。所有这些数据全部都是整数，而且不能再细分，也不能进一步提高他们的精确度

我们要如何使用seaborn绘制单变量分布呢？

首先，使用Numpy模块从标准整体分布中随机抽取1000个数，作为我们的连续数值型数据

data = np.random.normal(size=1000)

random是Numpy中一个随机模块，在random模块中的normal方法表示从正态分布中随机产生size数值

import numpy as np # 从正态分布中随机抽取数据 data = np.random.normal(size=1000) print(data) [-8.34177638e-01 -4.81175938e-01 -1.25574036e+00 -2.76567172e-01 1.12767332e+00 3.10682395e-01 7.80228852e-01 6.75012626e-01 7.73093289e-01 -8.19100385e-01 6.23650934e-01 -4.63975859e-01 8.05497705e-01 3.96842870e-01 7.08116405e-01 2.33275485e-01 1.64293668e-02 -8.21838448e-01 -7.55255648e-02 5.80824933e-01 4.19186101e-01 -1.76393595e-01 2.50923927e-01 2.38633140e+00 7.01521969e-02 -3.30433240e-01 1.87952220e-01 3.37810590e-01 1.91988625e-01 2.01966888e+00 -4.86049790e-01 1.19188356e+00 2.16887486e+00 9.15513599e-01 -2.48352719e+00 1.55961878e-01 1.31272700e+00 -1.62299621e+00 7.26296821e-01 1.37704098e+00 6.07926692e-02 -1.19652676e+00 3.76808682e-01 1.85074360e+00 -1.34054927e+00 -1.13845379e+00 5.20608532e-01 1.27837691e-01 1.77219926e+00 -2.55291654e-01 2.27639422e+00 1.27685372e+00 8.22275855e-01 -1.20431897e-01 1.81672628e-01 -1.57782301e-01 3.85544243e-01 -9.69654269e-01 3.44028022e-01 4.95341055e-01 2.79191801e-01 3.23729447e-01 -3.95386165e-01 4.66194790e-01 -1.33714386e-01 3.06156392e-01 -7.70696149e-01 8.19973046e-01 -1.00993542e+00 9.39025145e-01 7.62757615e-01 2.87180717e-01 2.25398644e-01 -1.59500193e+00 -1.10465930e-01 8.32425227e-01 -7.18909399e-01 -1.42882176e+00 -1.05234747e+00 2.62959536e-01 9.51529808e-01 -2.74716538e-01 8.60910473e-01 -2.19124055e-01 -2.10194292e+00 -3.77335381e-01 -7.51085854e-01 -8.81017955e-01 6.74371145e-01 5.98639252e-01 6.10331559e-01 7.14755842e-01 -5.11928294e-01 -2.30099728e-01 1.15677504e+00 -1.06007336e+00 -1.01522820e+00 1.71954293e-01 -8.12623339e-01 -7.30640934e-01 -1.64118008e+00 -6.49698686e-01 -1.70401886e-01 -1.53276763e-01 -4.22774099e-01 -2.24852915e-01 3.37953391e-01 4.07289668e-01 -4.61180680e-01 2.20948150e-01 -1.52689033e+00 1.04920136e+00 1.96450476e+00 -9.05873696e-01 1.61441603e+00 1.28356372e+00 -6.83415920e-01 -6.45630431e-01 8.99725304e-01 3.05129744e+00 2.19316668e-01 1.84002833e-01 -2.14166745e+00 -6.27041049e-01 1.47190685e+00 -7.39355868e-01 1.46162173e-01 -1.01737745e+00 1.00042232e+00 -2.01625500e-02 6.24353365e-01 -7.02306623e-02 6.04308724e-01 1.50763805e-01 -6.95982136e-01 -1.15902154e+00 7.03290461e-01 -1.14620607e+00 6.67298368e-02 2.04423004e-01 1.20562304e+00 5.50214883e-02 9.96904776e-02 -1.35437182e+00 3.16389099e-01 -4.39182774e-01 3.55920339e-01 -1.35057367e+00 1.48302682e+00 -8.69203659e-01 -6.58874553e-01 -4.23855028e-01 1.50421932e-01 -5.66152593e-01 3.45948614e-01 -7.47523838e-01 -1.19623426e+00 -3.98605965e-01 2.87306954e-01 -1.01637105e+00 -2.10535665e-01 -1.72303472e+00 -9.64750506e-02 -2.24236589e+00 -9.10371270e-01 -8.41739062e-01 1.09801149e+00 -9.42349346e-01 7.23165681e-01 7.70626473e-01 3.05736539e-01 -1.71333373e+00 1.92955450e+00 4.65400970e-02 -4.34492447e-02 2.31771452e-01 8.37837980e-01 1.08126421e+00 1.85025167e+00 -1.16840234e+00 9.68279908e-01 -2.62325281e-01 -1.53238798e+00 4.37177157e-01 5.63332241e-01 -3.72169682e-01 8.89878412e-01 8.06846027e-01 -3.26140782e-01 -1.29885394e+00 1.50610409e+00 -3.91506360e-01 2.64704947e-01 2.06004029e-01 6.29040471e-01 5.25936357e-01 -3.62670642e-01 -4.45307357e-02 1.73805342e+00 -1.51459412e+00 -6.23574644e-01 8.36734046e-01 -3.97223812e-01 2.04096634e-01 3.82344039e-01 -5.38329816e-01 -6.64114582e-01 -4.36281362e-01 -2.31595793e-01 -1.47293651e-01 -3.68346675e-01 1.13043248e+00 -5.42369689e-02 -4.59069394e-01 -1.19212085e-01 -1.53096134e+00 -2.80671335e-01 -2.14696939e+00 6.82354349e-01 6.95410918e-01 -1.08724145e+00 1.00653353e+00 1.99529922e+00 1.35160910e+00 2.23453409e+00 2.01207871e+00 -3.45560502e-01 -1.12993470e+00 -1.57660633e+00 -1.20479402e-01 -1.38121969e+00 -5.36648316e-01 -7.29825090e-01 2.42009456e+00 1.47857120e-01 -7.00427315e-01 -1.23994539e+00 -2.06448490e-01 -1.41790132e-02 5.83170802e-01 -1.51868606e-01 1.50646153e+00 1.96576757e-01 2.75617559e-01 -7.95265036e-01 -2.10836801e-01 -5.69139222e-01 -1.29864917e+00 5.63889982e-01 -1.55530862e+00 1.16185899e+00 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7.48487633e-01 -1.25607812e-01 -3.07932348e-01 -1.91174644e-02 5.35112221e-01 -9.41080295e-01 2.02867179e-03 7.46097449e-01 -1.43011900e+00 -4.59379731e-01 3.71134838e-01 1.21520575e+00 1.59019623e+00 -1.11857546e-01 1.59739705e+00 2.55413891e+00 -1.25147812e-01 -6.04724804e-01 -6.96143134e-02 3.48543388e-01 -1.41935629e-01 -1.78675601e+00 -1.34798677e-01 1.37510144e-01 -2.39196070e-01 9.94224828e-01 -3.45463996e-01 7.51274078e-01 1.13502040e+00 2.55126346e-01 2.10996865e+00 7.17082544e-01 7.11734850e-01 -9.16859807e-01 2.44011053e-01 -1.19199863e+00 -1.14214928e-01 4.77801219e-01 -1.02765923e+00 3.32053997e+00 8.55415155e-01 -8.89309190e-01 1.21371929e-01 -1.70169235e+00 7.14161089e-01 -1.94848200e+00 9.18053536e-01 1.06452917e+00 -2.84051701e-01 -1.69398111e-01 1.52642904e+00 9.97260703e-02 1.68755040e-02 2.33981992e-02 6.21840987e-02 9.67863268e-01 1.96911262e+00 -1.11623638e-01 1.80186406e+00 2.88796457e-01 -3.10158913e-01 -2.26802967e-01 6.83694320e-01 1.55915002e+00 1.02589790e+00 7.55330020e-01 2.38602920e+00 -3.45104266e-01 -4.87142197e-01 7.01321024e-01 1.34883283e+00 1.27381088e-01 3.13531268e-01 -5.13683489e-01 3.61069123e-01 -1.55837906e+00 -1.01359069e+00 -5.07144614e-01 6.16774319e-01 -4.30018462e-01 7.05357069e-01 1.78092198e+00 3.13208478e-01 -2.16426368e+00 -1.64000806e-01 -1.90645658e+00 1.79643622e+00 1.17069955e+00 4.55892115e-01 -4.89426360e-02 -1.01723438e+00 6

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Python数据分析第十二课：单变量、双变量及多变量分析图

Python数据分析第十二课：单变量、双变量及多变量分析图

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