数据分析案例一使用Python进行红酒与白酒数据数据分析

2024-06-01 13:04

本文主要是介绍数据分析案例一使用Python进行红酒与白酒数据数据分析,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

源码和数据集链接

以红葡萄酒为例

有两个样本:
winequality-red.csv:红葡萄酒样本
winequality-white.csv:白葡萄酒样本
每个样本都有得分从1到10的质量评分,以及若干理化检验的结果

#理化性质字段名称
1固定酸度fixed acidity
2挥发性酸度volatile acidity
3柠檬酸citric acid
4残糖residual sugar
5氯化物chlorides
6游离二氧化硫free sulfur dioxide
7总二氧化硫total sulfur dioxide
8密度density
9PH值pH
10硫酸盐sulphates
11酒精度alcohol
12质量quality

导入数据和库依赖

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# import seaborn as sns
%matplotlib inline
plt.style.use('ggplot')
# sep参数默认逗号
red_df = pd.read_csv('winequality-red.csv', sep=';')
white_df = pd.read_csv('winequality-white.csv', sep=';')
# 查看表头
red_df.head()
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur-dioxidedensitypHsulphatesalcoholquality
07.40.700.001.90.07611.034.00.99783.510.569.45
17.80.880.002.60.09825.067.00.99683.200.689.85
27.80.760.042.30.09215.054.00.99703.260.659.85
311.20.280.561.90.07517.060.00.99803.160.589.86
47.40.700.001.90.07611.034.00.99783.510.569.45

修改列名

发现 total_sulfur-dioxide 这个属性命名不规范,修改一下:

red_df.rename(columns={"total_sulfur-dioxide":"total_sulfur_dioxide"}, inplace=True)
# 查看修改成功
red_df.head(5)
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholquality
07.40.700.001.90.07611.034.00.99783.510.569.45
17.80.880.002.60.09825.067.00.99683.200.689.85
27.80.760.042.30.09215.054.00.99703.260.659.85
311.20.280.561.90.07517.060.00.99803.160.589.86
47.40.700.001.90.07611.034.00.99783.510.569.45

回答以下问题

  • 每个数据集中的样本数
  • 每个数据集中的列数
  • 具有缺少值的特征
  • 红葡萄酒数据集中的重复行
  • 数据集中的质量等级唯一值的数量
  • 红葡萄酒数据集的平均密度
# 查看基本信息
red_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1599 entries, 0 to 1598
Data columns (total 12 columns):
fixed_acidity           1599 non-null float64
volatile_acidity        1599 non-null float64
citric_acid             1599 non-null float64
residual_sugar          1599 non-null float64
chlorides               1599 non-null float64
free_sulfur_dioxide     1599 non-null float64
total_sulfur_dioxide    1599 non-null float64
density                 1599 non-null float64
pH                      1599 non-null float64
sulphates               1599 non-null float64
alcohol                 1599 non-null float64
quality                 1599 non-null int64
dtypes: float64(11), int64(1)
memory usage: 150.0 KB
# 查看样本数量
len(red_df)
1599
# 数据集中列数
len(red_df.columns)
12
# 红葡萄酒中重复行的数量
sum(red_df.duplicated())
240
# 质量的唯一值
len(red_df['quality'].unique())
6
# 红葡萄酒数据集中的平均密度
red_df['density'].mean()
0.9967466791744833

合并基本数据集

# 合并红、白葡萄酒的数据# 为红葡萄酒数据框创建颜色数组(生成多个新行)
color_red = np.repeat("red",red_df.shape[0])# 为白葡萄酒数据框创建颜色数组
color_white = np.repeat("white", white_df.shape[0])
len(color_red)
1599
red_df['color'] = color_red
# 查看新添加的列,发现添加成功
red_df.head()
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualitycolor
07.40.700.001.90.07611.034.00.99783.510.569.45red
17.80.880.002.60.09825.067.00.99683.200.689.85red
27.80.760.042.30.09215.054.00.99703.260.659.85red
311.20.280.561.90.07517.060.00.99803.160.589.86red
47.40.700.001.90.07611.034.00.99783.510.569.45red
white_df["color"] = color_white
white_df.head()
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualitycolor
07.00.270.3620.70.04545.0170.01.00103.000.458.86white
16.30.300.341.60.04914.0132.00.99403.300.499.56white
28.10.280.406.90.05030.097.00.99513.260.4410.16white
37.20.230.328.50.05847.0186.00.99563.190.409.96white
47.20.230.328.50.05847.0186.00.99563.190.409.96white
print(len(red_df))
print(len(white_df))
1599
4898
# 附加数据框
wine_df = red_df.append(white_df)# 查看数据框,检查是否成功
wine_df.head()
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualitycolor
07.40.700.001.90.07611.034.00.99783.510.569.45red
17.80.880.002.60.09825.067.00.99683.200.689.85red
27.80.760.042.30.09215.054.00.99703.260.659.85red
311.20.280.561.90.07517.060.00.99803.160.589.86red
47.40.700.001.90.07611.034.00.99783.510.569.45red
wine_df.shape
(6497, 13)

保存合并后的数据集

# 保存自己的数据集
wine_df.to_csv("winequality_edited.csv",index=False)
# 设置seaborn的样式
# sns.set_style("ticks")
wine_df = pd.read_csv("winequality_edited.csv")
wine_df.shape
(6497, 13)

可视化探索

  • 根据此数据集中的列的直方图,以下哪个特征变量显示为右偏态?固定酸度、总二氧化硫、pH 值、酒精度

hist方法详解
subplot返回值理解
subplot画图详解

绘制柱状图

fig, axs = plt.subplots(2, 2, figsize=(8, 8))#  _ 代表不分配名字的变量
_ = wine_df.fixed_acidity.plot.hist(ax=axs[0][0], rwidth=0.9)
_ = wine_df.total_sulfur_dioxide.plot.hist(ax=axs[0][1], rwidth=0.9)
_ = wine_df.pH.plot.hist(ax=axs[1][0], rwidth=0.9)
_ = wine_df.alcohol.plot.hist(ax=axs[1][1], rwidth=0.9)

image-20240531115344262

偏态的判定

下图依次表示左偏态、正态、右偏态

image-20240531114914904

wine_df.skew(axis=0)
fixed_acidity           1.723290
volatile_acidity        1.495097
citric_acid             0.471731
residual_sugar          1.435404
chlorides               5.399828
free_sulfur_dioxide     1.220066
total_sulfur_dioxide   -0.001177
density                 0.503602
pH                      0.386839
sulphates               1.797270
alcohol                 0.565718
quality                 0.189623
dtype: float64

偏度值为正,则为右偏态,说明fixed_acidity、pH、alcohol都是右偏态

  • 根据质量对不同特征变量的散点图,以下哪个最有可能对质量产生积极的影响?_挥发性酸度、残糖、pH 值、酒精度
x = wine_df[["fixed_acidity", "total_sulfur_dioxide", "pH", "alcohol", "quality"]]fig, axs = plt.subplots(2, 2, figsize=(12, 8))_  = x.plot.scatter(y='fixed_acidity', x='quality', ax=axs[0][0], linewidths=0.001, marker='o')
_  = x.plot.scatter(y='total_sulfur_dioxide', x='quality', ax=axs[0][1], linewidths=0.001, marker='o')
_  = x.plot.scatter(y='pH', x='quality', ax=axs[1][0], linewidths=0.001, marker='o')
_  = x.plot.scatter(y='alcohol', x='quality', ax=axs[1][1], linewidths=0.001, marker='o')# sns.despine()

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

从图上看其实并不是很明显,因此采用定量计算的方式,通过计算两个变量之间的相关系数,相关系数越大则越说明有积极影响

相关系数

sub_df = wine_df.iloc[:,np.r_[0,6,8,10,11]]
sub_df.corr()['quality']
fixed_acidity          -0.076743
total_sulfur_dioxide   -0.041385
pH                      0.019506
alcohol                 0.444319
quality                 1.000000
Name: quality, dtype: float64

发现alcohol的相关系数最大,说明起到的积极作用最大

查看平均值

wine_df.mean()
fixed_acidity             7.215307
volatile_acidity          0.339666
citric_acid               0.318633
residual_sugar            5.443235
chlorides                 0.056034
free_sulfur_dioxide      30.525319
total_sulfur_dioxide    115.744574
density                   0.994697
pH                        3.218501
sulphates                 0.531268
alcohol                  10.491801
quality                   5.818378
dtype: float64

按属性分组

# 按quality分组,查看每组均值
wine_df.groupby('quality').mean()
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcohol
quality
37.8533330.5170000.2810005.1400000.07703339.216667122.0333330.9957443.2576670.50633310.215000
47.2888890.4579630.2723154.1537040.06005620.636574103.4328700.9948333.2316200.50564810.180093
57.3268010.3896140.3077225.8041160.06466630.237371120.8391020.9958493.2121890.5264039.837783
67.1772570.3138630.3235835.5497530.05415731.165021115.4107900.9945583.2177260.53254910.587553
77.1289620.2888000.3347644.7316960.04527230.422150108.4986100.9931263.2280720.54702511.386006
86.8352330.2910100.3325395.3829020.04112434.533679117.5181350.9925143.2232120.51248711.678756
97.4200000.2980000.3860004.1200000.02740033.400000116.0000000.9914603.3080000.46600012.180000
# 分别以quality和color为两级索引进行分组,并查看均值
wine_df.groupby(['quality','color']).mean()
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcohol
qualitycolor
3red8.3600000.8845000.1710002.6350000.12250011.00000024.9000000.9974643.3980000.5700009.955000
white7.6000000.3332500.3360006.3925000.05430053.325000170.6000000.9948843.1875000.47450010.345000
4red7.7792450.6939620.1741512.6943400.09067912.26415136.2452830.9965423.3815090.59641510.265094
white7.1294480.3812270.3042334.6282210.05009823.358896125.2791410.9942773.1828830.47613510.152454
5red8.1672540.5770410.2436862.5288550.09273616.98384756.5139500.9971043.3049490.6209699.899706
white6.9339740.3020110.3376537.3349690.05154636.432052150.9045980.9952633.1688330.4822039.808840
6red8.3471790.4974840.2738242.4771940.08495615.71159940.8699060.9966153.3180720.67532910.629519
white6.8376710.2605640.3380256.4416060.04521735.650591137.0473160.9939613.1885990.49110610.575372
7red8.8723620.4039200.3751762.7206030.07658814.04522635.0201010.9961043.2907540.74125611.465913
white6.7347160.2627670.3256255.1864770.03819134.125568125.1147730.9924523.2138980.50310211.367936
8red8.5666670.4233330.3911112.5777780.06844413.27777833.4444440.9952123.2672220.76777812.094444
white6.6571430.2774000.3265145.6714290.03831436.720000126.1657140.9922363.2186860.48622911.636000
9white7.4200000.2980000.3860004.1200000.02740033.400000116.0000000.9914603.3080000.46600012.180000
# 分组属性不作为索引
wine_df.groupby(['quality','color'], as_index=False).mean()
qualitycolorfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcohol
03red8.3600000.8845000.1710002.6350000.12250011.00000024.9000000.9974643.3980000.5700009.955000
13white7.6000000.3332500.3360006.3925000.05430053.325000170.6000000.9948843.1875000.47450010.345000
24red7.7792450.6939620.1741512.6943400.09067912.26415136.2452830.9965423.3815090.59641510.265094
34white7.1294480.3812270.3042334.6282210.05009823.358896125.2791410.9942773.1828830.47613510.152454
45red8.1672540.5770410.2436862.5288550.09273616.98384756.5139500.9971043.3049490.6209699.899706
55white6.9339740.3020110.3376537.3349690.05154636.432052150.9045980.9952633.1688330.4822039.808840
66red8.3471790.4974840.2738242.4771940.08495615.71159940.8699060.9966153.3180720.67532910.629519
76white6.8376710.2605640.3380256.4416060.04521735.650591137.0473160.9939613.1885990.49110610.575372
87red8.8723620.4039200.3751762.7206030.07658814.04522635.0201010.9961043.2907540.74125611.465913
97white6.7347160.2627670.3256255.1864770.03819134.125568125.1147730.9924523.2138980.50310211.367936
108red8.5666670.4233330.3911112.5777780.06844413.27777833.4444440.9952123.2672220.76777812.094444
118white6.6571430.2774000.3265145.6714290.03831436.720000126.1657140.9922363.2186860.48622911.636000
129white7.4200000.2980000.3860004.1200000.02740033.400000116.0000000.9914603.3080000.46600012.180000
# 查看分组后pH属性所在列
wine_df.groupby(['quality','color'], as_index=False)['pH'].mean()
qualitycolorpH
03red3.398000
13white3.187500
24red3.381509
34white3.182883
45red3.304949
55white3.168833
66red3.318072
76white3.188599
87red3.290754
97white3.213898
108red3.267222
118white3.218686
129white3.308000

问题 1:某种类型的葡萄酒(红葡萄酒或白葡萄酒)是否代表更高的品质?

# 用 groupby 计算每个酒类型(红葡萄酒和白葡萄酒)的平均质量
wine_df.groupby("color")["quality"].mean()
color
red      5.636023
white    5.877909
Name: quality, dtype: float64

发现白葡萄酒的品质高于红葡萄酒

哪个酸度水平的平均评分最高?

# 用 Pandas 描述功能查看最小、25%、50%、75% 和 最大 pH 值
wine_df.pH.describe()
count    6497.000000
mean        3.218501
std         0.160787
min         2.720000
25%         3.110000
50%         3.210000
75%         3.320000
max         4.010000
Name: pH, dtype: float64
# 对用于把数据“分割”成组的边缘进行分组
bin_edges = [2.72, 3.11 ,3.21 ,3.32 ,4.01 ] # 用刚才计算的五个值填充这个列表
# 四个酸度水平组的标签
bin_names = [ "high", "median_high", "mediam", "low"] # 对每个酸度水平类别进行命名
help(pd.cut)
Help on function cut in module pandas.core.reshape.tile:cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise')Bin values into discrete intervals.Use `cut` when you need to segment and sort data values into bins. Thisfunction is also useful for going from a continuous variable to acategorical variable. For example, `cut` could convert ages to groups ofage ranges. Supports binning into an equal number of bins, or apre-specified array of bins.Parameters----------x : array-likeThe input array to be binned. Must be 1-dimensional.bins : int, sequence of scalars, or pandas.IntervalIndexThe criteria to bin by.* int : Defines the number of equal-width bins in the range of `x`. Therange of `x` is extended by .1% on each side to include the minimumand maximum values of `x`.* sequence of scalars : Defines the bin edges allowing for non-uniformwidth. No extension of the range of `x` is done.* IntervalIndex : Defines the exact bins to be used.right : bool, default TrueIndicates whether `bins` includes the rightmost edge or not. If``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``indicate (1,2], (2,3], (3,4]. This argument is ignored when`bins` is an IntervalIndex.labels : array or bool, optionalSpecifies the labels for the returned bins. Must be the same length asthe resulting bins. If False, returns only integer indicators of thebins. This affects the type of the output container (see below).This argument is ignored when `bins` is an IntervalIndex.retbins : bool, default FalseWhether to return the bins or not. Useful when bins is providedas a scalar.precision : int, default 3The precision at which to store and display the bins labels.include_lowest : bool, default FalseWhether the first interval should be left-inclusive or not.duplicates : {default 'raise', 'drop'}, optionalIf bin edges are not unique, raise ValueError or drop non-uniques... versionadded:: 0.23.0Returns-------out : pandas.Categorical, Series, or ndarrayAn array-like object representing the respective bin for each valueof `x`. The type depends on the value of `labels`.* True (default) : returns a Series for Series `x` or apandas.Categorical for all other inputs. The values stored withinare Interval dtype.* sequence of scalars : returns a Series for Series `x` or apandas.Categorical for all other inputs. The values stored withinare whatever the type in the sequence is.* False : returns an ndarray of integers.bins : numpy.ndarray or IntervalIndex.The computed or specified bins. Only returned when `retbins=True`.For scalar or sequence `bins`, this is an ndarray with the computedbins. If set `duplicates=drop`, `bins` will drop non-unique bin. Foran IntervalIndex `bins`, this is equal to `bins`.See Also--------qcut : Discretize variable into equal-sized buckets based on rankor based on sample quantiles.pandas.Categorical : Array type for storing data that come from afixed set of values.Series : One-dimensional array with axis labels (including time series).pandas.IntervalIndex : Immutable Index implementing an ordered,sliceable set.Notes-----Any NA values will be NA in the result. Out of bounds values will be NA inthe resulting Series or pandas.Categorical object.Examples--------Discretize into three equal-sized bins.>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3)... # doctest: +ELLIPSIS[(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...Categories (3, interval[float64]): [(0.994, 3.0] < (3.0, 5.0] ...>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3, retbins=True)... # doctest: +ELLIPSIS([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...Categories (3, interval[float64]): [(0.994, 3.0] < (3.0, 5.0] ...array([0.994, 3.   , 5.   , 7.   ]))Discovers the same bins, but assign them specific labels. Notice thatthe returned Categorical's categories are `labels` and is ordered.>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]),...        3, labels=["bad", "medium", "good"])[bad, good, medium, medium, good, bad]Categories (3, object): [bad < medium < good]``labels=False`` implies you just want the bins back.>>> pd.cut([0, 1, 1, 2], bins=4, labels=False)array([0, 1, 1, 3])Passing a Series as an input returns a Series with categorical dtype:>>> s = pd.Series(np.array([2, 4, 6, 8, 10]),...               index=['a', 'b', 'c', 'd', 'e'])>>> pd.cut(s, 3)... # doctest: +ELLIPSISa    (1.992, 4.667]b    (1.992, 4.667]c    (4.667, 7.333]d     (7.333, 10.0]e     (7.333, 10.0]dtype: categoryCategories (3, interval[float64]): [(1.992, 4.667] < (4.667, ...Passing a Series as an input returns a Series with mapping value.It is used to map numerically to intervals based on bins.>>> s = pd.Series(np.array([2, 4, 6, 8, 10]),...               index=['a', 'b', 'c', 'd', 'e'])>>> pd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False)... # doctest: +ELLIPSIS(a    0.0b    1.0c    2.0d    3.0e    4.0dtype: float64, array([0, 2, 4, 6, 8]))Use `drop` optional when bins is not unique>>> pd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True,...    right=False, duplicates='drop')... # doctest: +ELLIPSIS(a    0.0b    1.0c    2.0d    3.0e    3.0dtype: float64, array([0, 2, 4, 6, 8]))Passing an IntervalIndex for `bins` results in those categories exactly.Notice that values not covered by the IntervalIndex are set to NaN. 0is to the left of the first bin (which is closed on the right), and 1.5falls between two bins.>>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])>>> pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)[NaN, (0, 1], NaN, (2, 3], (4, 5]]Categories (3, interval[int64]): [(0, 1] < (2, 3] < (4, 5]]

# 创建 acidity_levels 列
wine_df['acidity_levels'] = pd.cut(wine_df['pH'], bin_edges, labels=bin_names)# 检查该列是否成功创建
wine_df.head()
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualitycoloracidity_levels
07.40.700.001.90.07611.034.00.99783.510.569.45redlow
17.80.880.002.60.09825.067.00.99683.200.689.85redmedian_high
27.80.760.042.30.09215.054.00.99703.260.659.85redmediam
311.20.280.561.90.07517.060.00.99803.160.589.86redmedian_high
47.40.700.001.90.07611.034.00.99783.510.569.45redlow
# 用 groupby 计算每个酸度水平的平均质量
wine_df.groupby("acidity_levels")['quality'].mean()
acidity_levels
high           5.783343
median_high    5.784540
mediam         5.850832
low            5.859593
Name: quality, dtype: float64

发现酸度越低,质量评分就越好

# 保存更改,供下一段使用
wine_df.to_csv('winequality_edited_al.csv', index=False)

酒精含量高的酒是否评分较高?

# 获取酒精含量的中位数
alcohol_median = wine_df.alcohol.median()
wine_df.head();
# 选择酒精含量小于中位数的样本
low_alcohol = wine_df.query("alcohol < @alcohol_median")# 选择酒精含量大于等于中位数的样本
high_alcohol = wine_df.query("alcohol >= @alcohol_median")
# 获取低酒精含量组和高酒精含量组的平均质量评分
print("低浓度酒精:",low_alcohol.quality.mean())
print("高浓度酒精:", high_alcohol.quality.mean())
低浓度酒精: 5.475920679886686
高浓度酒精: 6.146084337349397

发现高浓度酒精的质量评级更高

口感较甜的酒是否评分较高?

# 获取残留糖分的中位数
sugar_median = wine_df["residual_sugar"].median()
# 选择残留糖分小于中位数的样本
low_sugar = wine_df.query("residual_sugar < @sugar_median")# 选择残留糖分大于等于中位数的样本
high_sugar = wine_df.query("residual_sugar >= @sugar_median")# 确保这些查询中的每个样本只出现一次
num_samples = wine_df.shape[0]
num_samples == low_sugar['quality'].count() + high_sugar['quality'].count() # 应为真
True
# 获取低糖分组和高糖分组的平均质量评分
print("高糖分质量评分:",high_sugar.quality.mean())
print("低糖分质量评分:",low_sugar.quality.mean())
高糖分质量评分: 5.82782874617737
低糖分质量评分: 5.808800743724822

发现高糖分的酒质量评分更高

类和质量图

Seaborn绘图示例
Pandas可视化文档

首先查看一下两种酒的质量均值

colors = ['red','white']
color_means = wine_df.groupby('color')['quality'].mean()
color_means.plot(kind='bar', title='Average Wine Quality by Color', color=colors, alpha=.8)
plt.xlabel('colors', fontsize=18);
plt.ylabel('Quality', fontsize=18);

output_79_0

进一步按质量和颜色分组查看

counts = wine_df.groupby(['quality', 'color']).count()['pH']
counts.plot(kind='bar', title='Counts by Wine Color and quality', color=counts.index.get_level_values(1), alpha=.7)
plt.xlabel('Quality and Color', fontsize=18)
plt.ylabel('Count', fontsize=18)
Text(0, 0.5, 'Count')

output_81_1

但红酒和白酒的样本数本来就相差较大,所以我们查看比例才更准确。

totals = wine_df.groupby('color').count()['pH']
counts = wine_df.groupby(['quality', 'color']).count()['pH']
proportions = counts / totals
proportions.plot(kind='bar', title='Counts by Wine Color and quality',color=counts.index.get_level_values(1), alpha=.7)
plt.xlabel('Quality and Color', fontsize=18)
plt.ylabel('Proportions', fontsize=18)
Text(0, 0.5, 'Proportions')

output_83_1

# 用 Matplotlib 创建柱状图

pyplot 的 bar 功能中有两个必要参数:条柱的 x 坐标和条柱的高度。

plt.bar([1, 2, 3], [224, 620, 425], color='blue');

output_86_0

可以利用 pyplot 的 xticks 功能,或通过在 bar 功能中指定另一个参数,指定 x 轴刻度标签。以下两个框的结果相同。

# 绘制条柱
plt.bar([1, 2, 3], [224, 620, 425])# 为 x 轴指定刻度标签及其标签
plt.xticks([1, 2, 3], ['a', 'b', 'c']);

output_88_0

# 用 x 轴的刻度标签绘制条柱
plt.bar([1, 2, 3], [224, 620, 425], tick_label=['a', 'b', 'c']);

output_89_0

用以下方法设置轴标题和标签。

plt.bar([1, 2, 3], [224, 620, 425], tick_label=['a', 'b', 'c'])
plt.title('Some Title')
plt.xlabel('Some X Label')
plt.ylabel('Some Y Label');


output_91_0

# example
import matplotlib.pyplot as plt
import numpy as npx = np.linspace(0, 1, 10)
number = 5
cmap = plt.get_cmap('gnuplot')
colors = [cmap(i) for i in np.linspace(0, 1, number)]for i, color in enumerate(colors, start=1):plt.plot(x, i * x + i, color=color, label='$y = {i}x + {i}$'.format(i=i))
plt.legend(loc='best')
plt.show()


外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

这篇关于数据分析案例一使用Python进行红酒与白酒数据数据分析的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/1021081

相关文章

MySQL 中的 JSON 查询案例详解

《MySQL中的JSON查询案例详解》:本文主要介绍MySQL的JSON查询的相关知识,本文给大家介绍的非常详细,对大家的学习或工作具有一定的参考借鉴价值,需要的朋友参考下吧... 目录mysql 的 jsON 路径格式基本结构路径组件详解特殊语法元素实际示例简单路径复杂路径简写操作符注意MySQL 的 J

Java学习手册之Filter和Listener使用方法

《Java学习手册之Filter和Listener使用方法》:本文主要介绍Java学习手册之Filter和Listener使用方法的相关资料,Filter是一种拦截器,可以在请求到达Servl... 目录一、Filter(过滤器)1. Filter 的工作原理2. Filter 的配置与使用二、Listen

Pandas使用AdaBoost进行分类的实现

《Pandas使用AdaBoost进行分类的实现》Pandas和AdaBoost分类算法,可以高效地进行数据预处理和分类任务,本文主要介绍了Pandas使用AdaBoost进行分类的实现,具有一定的参... 目录什么是 AdaBoost?使用 AdaBoost 的步骤安装必要的库步骤一:数据准备步骤二:模型

Pandas统计每行数据中的空值的方法示例

《Pandas统计每行数据中的空值的方法示例》处理缺失数据(NaN值)是一个非常常见的问题,本文主要介绍了Pandas统计每行数据中的空值的方法示例,具有一定的参考价值,感兴趣的可以了解一下... 目录什么是空值?为什么要统计空值?准备工作创建示例数据统计每行空值数量进一步分析www.chinasem.cn处

使用Pandas进行均值填充的实现

《使用Pandas进行均值填充的实现》缺失数据(NaN值)是一个常见的问题,我们可以通过多种方法来处理缺失数据,其中一种常用的方法是均值填充,本文主要介绍了使用Pandas进行均值填充的实现,感兴趣的... 目录什么是均值填充?为什么选择均值填充?均值填充的步骤实际代码示例总结在数据分析和处理过程中,缺失数

如何使用 Python 读取 Excel 数据

《如何使用Python读取Excel数据》:本文主要介绍使用Python读取Excel数据的详细教程,通过pandas和openpyxl,你可以轻松读取Excel文件,并进行各种数据处理操... 目录使用 python 读取 Excel 数据的详细教程1. 安装必要的依赖2. 读取 Excel 文件3. 读

Python的time模块一些常用功能(各种与时间相关的函数)

《Python的time模块一些常用功能(各种与时间相关的函数)》Python的time模块提供了各种与时间相关的函数,包括获取当前时间、处理时间间隔、执行时间测量等,:本文主要介绍Python的... 目录1. 获取当前时间2. 时间格式化3. 延时执行4. 时间戳运算5. 计算代码执行时间6. 转换为指

利用Python调试串口的示例代码

《利用Python调试串口的示例代码》在嵌入式开发、物联网设备调试过程中,串口通信是最基础的调试手段本文将带你用Python+ttkbootstrap打造一款高颜值、多功能的串口调试助手,需要的可以了... 目录概述:为什么需要专业的串口调试工具项目架构设计1.1 技术栈选型1.2 关键类说明1.3 线程模

Python ZIP文件操作技巧详解

《PythonZIP文件操作技巧详解》在数据处理和系统开发中,ZIP文件操作是开发者必须掌握的核心技能,Python标准库提供的zipfile模块以简洁的API和跨平台特性,成为处理ZIP文件的首选... 目录一、ZIP文件操作基础三板斧1.1 创建压缩包1.2 解压操作1.3 文件遍历与信息获取二、进阶技

Python Transformers库(NLP处理库)案例代码讲解

《PythonTransformers库(NLP处理库)案例代码讲解》本文介绍transformers库的全面讲解,包含基础知识、高级用法、案例代码及学习路径,内容经过组织,适合不同阶段的学习者,对... 目录一、基础知识1. Transformers 库简介2. 安装与环境配置3. 快速上手示例二、核心模