pandas与matplotlib综合案例

Posted 2023-04-06

参考技术A

需求：分析Xiaoqu_NJ.csv文件，分析南京各小区分布数量、及小区建成情况等。

1、打开Xiaoqu_NJ.csv文件，由于爬取的文件没有表头，所以需要自定义表头。

2、城市定位与街道定位去除’小区‘

3、删除多余的列

4、将热度等字段保留数字，其余删除。用到extract()文本提取函数，它与正则表达式配合使用。\\d+，其中\\d代表数字，+代表批配数字后的数字

5、物业费。保留单价数字，用到
data[\'物业费\'] = data[\'物业费\'].str.extract(\'(. ?)元. ?\')
（.*？)提取元前（）内的数据

6、永久删除缺失值

7、增加一列使用年限

8、保存清洗 后的数据

1、导入库

2、读取修改后的数据

3、绘制南京各区小区分布数量图
数组转列表，.tolist()

运行效果：

4、南京小区建成数量与年份的关系图表

运行效果：

运行效果：

5、各区域建成数量与年份关系图

运行效果：

6、南京下辖各区小区均价情况

运行效果

pandas案例分析,附加numpy matplotlib

import pandas as pd

df=pd.read_csv(,sep=;)

这是如果出现;  说明是用;做分隔符，而不是默认的,

 

import pandas as pd
red_df = pd.read_csv(winequality-red.csv, sep=;)
white_df = pd.read_csv(winequality-white.csv, sep=;)
red_df.head()
white_df.head()

fixed_acidity	volatile_acidity	citric_acid	residual_sugar	chlorides	free_sulfur_dioxide	total_sulfur_dioxide	density	pH	sulphates	alcohol	quality
0	7.0	0.27	0.36	20.7	0.045	45.0	170.0	1.0010	3.00	0.45	8.8	6
1	6.3	0.30	0.34	1.6	0.049	14.0	132.0	0.9940	3.30	0.49	9.5	6
2	8.1	0.28	0.40	6.9	0.050	30.0	97.0	0.9951	3.26	0.44	10.1	6
3	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.9956	3.19	0.40	9.9	6
4	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.9956	3.19	0.40	9.9	6

 

print(red_df.shape)

(1599, 12)

print(white_df.shape)

(4898, 12)

red_df.isnull().sum()

fixed_acidity 0 volatile_acidity 0 citric_acid 0 residual_sugar 0 chlorides 0 free_sulfur_dioxide 0 total_sulfur-dioxide 0 density 0 pH 0 sulphates 0 alcohol 0 quality 0 dtype: int64

white_df.isnull().sum()

fixed_acidity 0 volatile_acidity 0 citric_acid 0 residual_sugar 0 chlorides 0 free_sulfur_dioxide 0 total_sulfur_dioxide 0 density 0 pH 0 sulphates 0 alcohol 0 quality 0 dtype: int64

white_df.duplicated().sum() 重复值统计

937 但是重复行是不可以删除的

红葡萄酒数据集中有多少唯一的质量值？

red_df.quality.nunique()

6

红葡萄酒数据集中的平均密度是多少？

red_df.density.mean()

0.996746679174484

 

import numpy as np

a=np.random.random(1000)

生成1000个随机数的矩阵

np.mean(a)

求a得平均值
 

 

# 导入 numpy 和 pandas import numpy as np import pandas as pd # 加载红葡萄酒和白葡萄酒数据集 red_df = pd.read_csv(winequality-red.csv, sep=;) white_df = pd.read_csv(winequality-white.csv, sep=;)

# 为红葡萄酒数据框创建颜色数组
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])

red_df[color] = color_red
red_df.head()

fixed_acidity	volatile_acidity	citric_acid	residual_sugar	chlorides	free_sulfur_dioxide	total_sulfur-dioxide	density	pH	sulphates	alcohol	quality	color
0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5	red
1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5	red
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5	red
3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6	red
4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5	red

 

# 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()

alcohol	chlorides	citric_acid	color	density	fixed_acidity	free_sulfur_dioxide	pH	quality	residual_sugar	sulphates	total_sulfur-dioxide	total_sulfur_dioxide	volatile_acidity
0	9.4	0.076	0.00	red	0.9978	7.4	11.0	3.51	5	1.9	0.56	34.0	NaN	0.70
1	9.8	0.098	0.00	red	0.9968	7.8	25.0	3.20	5	2.6	0.68	67.0	NaN	0.88
2	9.8	0.092	0.04	red	0.9970	7.8	15.0	3.26	5	2.3	0.65	54.0	NaN	0.76
3	9.8	0.075	0.56	red	0.9980	11.2	17.0	3.16	6	1.9	0.58	60.0	NaN	0.28
4	9.4	0.076	0.00	red	0.9978	7.4	11.0	3.51	5	1.9	0.56	34.0	NaN	0.70

 

 

将新组合的数据框保存为 ​​winequality_edited.csv​​​。务必设置 ​​index=False​​，以避免保存未命名列！

wine_df.to_csv(winequality_edited.csv, index=False)

 

new_labels=list(red_df.columns)

new_labels[6]=total_sulfur_dioxide

red_df.columns=new_labels

 

groupby函数

 

red_df.groupby(quality).mean()

求出平均值

red_df.groupby([quality,color]).mean()

red_df.groupby([quality,color],as_index=False).mean()

不用颜色和质量做索引as_index=False

只对某一列做平均值

red_df.groupby([quality,color],as_index=False)[ph].mean()

 

df.groupby(color).mean().quality

df.describe().pH

bin_edges = [2.72, 3.11, 3.21, 3.32, 4.01]

bin_names = [high, mod_high, medium, low]

df[acidity_levels] = pd.cut(df[pH], bin_edges, labels=bin_names)

df.head()

df.groupby(acidity_levels).mean().quality

df.to_csv(winequality_edited.csv, index=False)

等效语句

# selecting malignant records in cancer data
df_m = df[df[diagnosis] == M]
df_m = df.query(diagnosis == "M")

# selecting records of people making over $50K
df_a = df[df[income] ==  >50K]
df_a = df.query(income == " >50K")

# get the median amount of alcohol content

# 获取酒精含量的中位数 df.alcohol.median()

# 选择酒精含量小于中位数的样本
low_alcohol =df[df.alcohol < 10.3]

# 选择酒精含量大于等于中位数的样本
high_alcohol =df[df.alcohol >= 10.3]

# 确保这些查询中的每个样本只出现一次
num_samples = df.shape[0]
num_samples == low_alcohol[quality].count() + high_alcohol[quality].count() # 应为真

# 获取低酒精含量组和高酒精含量组的平均质量评分

low_alcohol.quality.mean(), high_alcohol.quality.mean()

# 获取残留糖分的中位数

df.residual_sugar.median()

# 选择残留糖分小于中位数的样本
low_sugar =df[df.residual_sugar < 3]

# 选择残留糖分大于等于中位数的样本
high_sugar =df[df.residual_sugar >= 3]

# 确保这些查询中的每个样本只出现一次
num_samples == low_sugar[quality].count() + high_sugar[quality].count() # 应为真

 

# 获取低糖分组和高糖分组的平均质量评分
low_sugar.quality.mean(), high_sugar.quality.mean()

 

colors=[red,white]

wine_df.groupby(color)[quality].mean().plot(kkind=bar,title=ceshi1,colors=[red,white],alpha=.7)

 

 

 

引入sns  matplotlib 

import pandas as pd

import matplotlib.pyplot  as  plt

import seaborn as sns

%matplotlib inline

...

...

...

colors=[red,white]

color_means=wine_df.groupby(color)[quality].mean()

color_means.plot(kkind=bar,title=ceshi1,colors=colors,alpha=.7)

plt.xlabel("colors",fontsize=18)

plt.ylabel("colors",fontsize=18)

 

 

 

counts= wine_df.groupby([quality,color]).count()[pH]

counts

totals=wine_df.groupby(color).count()[pH]

proportions = counts /totals

proportions.plot(kind=bar,title=ceshi1,colors=colors,alpha=.7)

 

 

 

import matplotlib.pyplot as plt
% matplotlib inline

plt.bar([1, 2, 3], [224, 620, 425]);

 

# 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]);

# 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]);

 

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);

 

# 用查询功能选择每个组，并获取其平均质量
median = df[alcohol].median()
low = df.query(alcohol < .format(median))
high = df.query(alcohol >= .format(median))

mean_quality_low = low[quality].mean()
mean_quality_high = high[quality].mean()

 

# 用合适的标签创建柱状图
locations = [1, 2]
heights = [mean_quality_low, mean_quality_high]
labels = [Low, High]
plt.bar(locations, heights, tick_label=labels)
plt.title(Average Quality Ratings by Alcohol Content)
plt.xlabel(Alcohol Content)
plt.ylabel(Average Quality Rating);

 

 

用 Matplotlib 绘制酒的类型和质量视图

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline
import seaborn as sns
sns.set_style(darkgrid)

wine_df = pd.read_csv(winequality_edited.csv)

# 获取每个等级和颜色的数量
color_counts = wine_df.groupby([color, quality]).count()[pH]
color_counts

# 获取每个颜色的总数
color_totals = wine_df.groupby(color).count()[pH]
color_totals

# 将红葡萄酒等级数量除以红葡萄酒样本总数，获取比例
red_proportions = color_counts[red] / color_totals[red]
red_proportions

# 将白葡萄酒等级数量除以白葡萄酒样本总数，获取比例
white_proportions = color_counts[white] / color_totals[white]
white_proportions

ind = np.arange(len(red_proportions))  # 组的 x 坐标位置
width = 0.35       # 条柱的宽度

# 绘制条柱
red_bars = plt.bar(ind, red_proportions, width, color=r, alpha=.7, label=Red Wine)
white_bars = plt.bar(ind + width, white_proportions, width, color=w, alpha=.7, label=White Wine)

# 标题和标签
plt.ylabel(Proportion)
plt.xlabel(Quality)
plt.title(Proportion by Wine Color and Quality)
locations = ind + width / 2  # x 坐标刻度位置
labels = [3, 4, 5, 6, 7, 8, 9]  # x 坐标刻度标签
plt.xticks(locations, labels)

# 图例
plt.legend()

 

red_proportions[9] = 0
red_proportions