1. Residential consumption index
1. Data sources
Data source website used: National Bureau of Statistics
Data website link: https://data.stats.gov.cn/easyquery.htm?cn=A01
2. Download data
Click the download button:
Register an account of the National Bureau of Statistics, then automatically log in and jump to the data page, click download again, and select the csv format:
It can be downloaded successfully and opened in the folder:
Open the file and we can see the data:
3. Adjust the data
A copy of the source data is placed in the project, and unnecessary data is deleted (we only keep the data for the whole year of 2022)
4. Central tendency analysis: mean, median, upper quartile, lower quartile
5. Calculation of range, quartile, variance, standard deviation, coefficient of variation
6. Central tendency analysis, dispersion degree analysis, distribution shape analysis
1. Central trend analysis
The consumer price index for the whole year of 2022 is concentrated in the range of 100.9~102.8, the
consumer price index of food, tobacco and alcohol is concentrated in the range of 98.2~106.3, and
the consumer price index of clothing is concentrated in the range of 100.4~100.7 Within the range,
the residential consumer price index is in the range of 99.8~101.4, the
consumer price index of daily necessities and services is in the range of 100.4~101.5, the consumer price index of
transportation and communication is in the range of 102.8~108.5, and the consumer price index of
education, culture and entertainment is in the range of 102.8~108.5. The consumer price index of residents is in the range of 101.2~102.9,
the consumer price index of medical care is in the range of 100.5~100.7, and
the consumer price index of other goods and services is in the range of 99.9~102.8.
2. Dispersion analysis
The consumer price index for the whole year of 2022 is distributed at 100.9, 101.5, 101.6, 101.8, 102.1, 102.5, 102.7, and 102.8; the
consumer price index for food, tobacco and alcohol is distributed at 98.2 and 99.7 , 101.9, 102.1, 102.5, 103, 103.7, 104.5, 104.7, 105.2, and 106.3; the
clothing consumer price index is distributed at 100.4, 100.5, 100.6, and 100.7; the
residential consumer price index The index is distributed at 99.8, 100.3, 100.6, 100.7, 100.8, 101, 101.2, 101.3, 101.4; the
consumer price index of daily necessities and services is distributed at 100.4, 100.6, 100.9, 101.2, 101.3, 101.4, 101.5 these few numbers;
Consumer prices and indices for transportation and communications are distributed at 102.8, 102.9, 103.1, 104.5, 104.9, 105.2, 105.5, 105.8, 106.1, 106.2, 106.5, and 108.5;
consumer price indices for education, culture and entertainment are distributed On the numbers of 101.2, 101.3, 101.4, 101.5, 101.6, 101.8, 102, 102.1, 102.5, 102.6, and 102.9; the
consumer price index of medical care is distributed on the numbers of 100.5, 100.6, and 100.7;
other supplies The consumer price index of households and services is distributed in the numbers of 99.9, 100.3, 100.9, 101.6, 101.7, 101.8, 101.9, 102.1, 102.2, 102.3, and 102.8.
3. Distribution shape analysis
Numerical values are concentrated between 98 and 108, with 100 as the cut-off point, there are many index values greater than 100, and only a few are less than 100.
7. Use Matplotlib to draw histograms, line charts, histograms (bar charts), pie charts, box plots, scatter plots, and regression charts
1. Histogram
2. Line chart
3. Histogram (bar graph)
4. Pie chart
5. Boxplot
A boxplot contains a set of data: median, upper quartile, lower quartile, inner limit, outer limit, outliers.
6. Scatterplot
7. Regression graph
8. Use Seaborn to draw histograms, line charts, histograms (bar charts), pie charts, box plots, scatter plots, and regression charts
1. Histogram
2. Line chart
3. Histogram (bar graph)
4. Pie chart
5. Boxplot
6. Scatterplot
7. Regression graph
9. Source code
import numpy as np
import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# 四分位数有三个，第一个四分位数称为下四分位数，第二个四分位数就是中位数，第三个四分位数称为上四分位数
def quantile_exc(data, n): # 其中data为数据组，n为第几个四分位数
if n < 1 or n > 3:
return False
data.sort() # 给数据排序
position = (len(data) + 1)*n/4
pos_integer = int(math.modf(position)[1])
pos_decimal = position - pos_integer
quartile = data[pos_integer - 1] + (data[pos_integer] - data[pos_integer - 1])*pos_decimal
return quartile
def get_range(data_list): # 得到极差
data_list.sort()
return data_list[-1] - data_list[0]
def get_value_and_count(data_list): # 得到数值以及其计数
data_list.sort()
no_repeat_list = []
for item in data_list:
if item not in no_repeat_list:
no_repeat_list.append(item)
for item in no_repeat_list:
count = data_list.count(item)
print(item, ": ", count)
if __name__ == "__main__":
# 1. 从csv文件中读取数据
with open("./2022年月度数据.csv", "r", encoding="gbk") as f:
head = f.readline().strip().split(",") # 横行表头
# print(head)
data_list = [item.strip().split(",") for item in f.readlines()] # 数据内容列表
# print(data_list)
# 将数据列表转换成字典：
data_dict = {
}
for item in data_list:
data_dict[item[0]] = [eval(item) for item in item[1:]]
# print(data_dict)
# 遍历使用到的数据：
for name, data in data_dict.items():
print(name, data)
# 2. 计算均值、中位数、上及下四分位数 ==================================
# 2.1 计算均值：
print("----------------均值----------------------")
means_dict = {
}
for name, data in data_dict.items():
name += "_均值"
mean = round(np.mean(data), 2)
means_dict[name] = mean
print(name, ": ", mean) # 名称：均值
# print(means_dict)
# 2.2 计算中位数（排序统计中间值）
print("----------------中位数----------------------")
median_dict = {
}
for name, data in data_dict.items():
name += "_中位数"
a_median = round(quantile_exc(data, 2), 2)
median_dict[name] = a_median
print(name, ": ", a_median) # 名称：中位数
# 2.3 计算上四分位数
print("---------------上四分位数-------------------")
up_quantile_dict = {
}
for name, data in data_dict.items():
name += "_上四分位数"
a_quantile = round(quantile_exc(data, 3), 2) # 保留两位小数
up_quantile_dict[name] = a_quantile
print(name, ": ", a_quantile) # 名称：上四分位数
# 2.4 计算下四分位数
print("---------------下四分位数-------------------")
down_quantile_dict = {
}
for name, data in data_dict.items():
name += "_下四分位数"
a_quantile = round(quantile_exc(data, 1), 2) # 保留两位小数
down_quantile_dict[name] = a_quantile
print(name, ": ", a_quantile) # 名称：上四分位数
# 3. 计算极差、四分位差、方差、标准差、变异系数
# 3.1 计算极差：最大值-最小值
print("--------------极差---------------")
range_dict = {
}
for name, data in data_dict.items():
name += "_极差"
a_range = round(get_range(data), 2)
range_dict[name] = a_range
print(name, ": ", a_range)
# 3.2 计算四分位差： 上四分位数-下四分位数
print("--------------四分位差------------")
interquartile_difference = {
}
for name in data_dict.keys():
name1 = name +"_四分位差"
name2 = name + "_上四分位数"
name3 = name + "_下四分位数"
for i in range(len(data_dict)):
a_interqurtile = round(up_quantile_dict[name2] - down_quantile_dict[name3], 2)
interquartile_difference[name1] = a_interqurtile
print(name, ": ", a_interqurtile)
# 3.3 计算方差
print("--------------方差----------------")
var_dict = {
}
for name, data in data_dict.items():
name += "_方差"
a_var = round(np.var(data), 2)
var_dict[name] = a_var
print(name, ": ", a_var)
# 3.4 计算标准差
print("--------------标准差----------------")
std_dict = {
}
for name, data in data_dict.items():
name += "_标准差"
a_std = round(np.std(data, ddof=1), 2)
std_dict[name] = a_std
print(name, ": ", a_std)
# 3.5 计算变异系数：标准差/均值
print("-------------变异系数---------------")
coefficient_dict = {
}
for name in data_dict.keys():
name1 = name + "_变异系数"
name2 = name + "_标准差"
name3 = name + "_均值"
for i in range(len(data_dict)):
a_coefficient = std_dict[name2]/means_dict[name3]
coefficient_dict[name1] = a_coefficient
print(name, ": ", a_coefficient)
# 4. 集中趋势分析、离散程度分析、分布形状分析
# 4.1 集中趋势分析：统计不同指数的出现次数
print("------------集中和离散趋势分析-------------")
central_trend_analysis = ""
for name, data in data_dict.items():
print(name, ":")
get_value_and_count(data)
# 5. 用Matplotlib和Seaborn绘制直方图、折线图、柱状图（条形图）、饼图、箱线图、散点图、回归图
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
# 5.1 绘制直方图
# （1）matplotlib
all_data = []
for name, data in data_dict.items():
all_data += data
# print(all_data)
plt.style.use("fivethirtyeight")
plt.hist(all_data, bins=5, edgecolor="black")
plt.title('2022年全年各居民消费指数分布')
plt.xlabel('指数')
plt.ylabel('数量')
plt.tight_layout()
plt.show()
# （2）seaborn
sns.set_context({
'figure.figsize': [7, 7]})
rc = {
'font.sans-serif': 'SimHei',
'axes.unicode_minus': False}
sns.set(context="notebook", style="ticks", rc=rc)
dis = sns.distplot(all_data, bins=5, kde=False, rug=True)
dis.set_title("2022年全年各居民消费指数分布")
dis.set_xlabel("指数", fontsize=10)
dis.set_ylabel("数量", fontsize=10)
# 5.2 绘制折线图
# (1) matplotlib
plt.figure(figsize=(10, 10))
data1 = data_dict["居民消费价格指数"]
data2 = data_dict["食品烟酒类居民消费价格指数"]
data3 = data_dict["衣着类居民消费价格指数"]
data4 = data_dict["居住类居民消费价格指数"]
data5 = data_dict["生活用品及服务类居民消费价格指数"]
data6 = data_dict["交通和通信类居民消费价格指数"]
data7 = data_dict["教育文化和娱乐类居民消费价格指数"]
data8 = data_dict["医疗保健类居民消费价格指数"]
data9 = data_dict["其他用品和服务类居民消费价格指数"]
x = range(12)
x_ticks = [f"{
i}月" for i in range(1, 13)]
plt.xticks(x, x_ticks)
plt.ylabel("指数值") # 给y轴加名称
# 添加标题
plt.title("2022年居民消费指数")
plt.plot(x, data1, label="居民消费价格指数")
plt.plot(x, data2, label="食品烟酒类居民消费价格指数")
plt.plot(x, data3, label="衣着类居民消费价格指数")
plt.plot(x, data4, label="居住类居民消费价格指数")
plt.plot(x, data5, label="生活用品及服务类居民消费价格指数")
plt.plot(x, data6, label="交通和通信类居民消费价格指数")
plt.plot(x, data7, label="教育文化和娱乐类居民消费价格指数")
plt.plot(x, data8, label="医疗保健类居民消费价格指数")
plt.plot(x, data9, label="其他用品和服务类居民消费价格指数")
plt.legend(loc="best")
plt.show()
# (2) seaborn
sns.set_context({
'figure.figsize': [11, 7]})
# 显示所有列
pd.set_option('display.max_columns', None)
# 显示所有行
pd.set_option('display.max_rows', None)
df = pd.DataFrame(data_dict)
df.insert(9, "月份", [i for i in range(1, 13)], allow_duplicates=False)
print(df)
sns.lineplot(x="月份", y="居民消费价格指数", data=df)
plt.show()
sns.lineplot(x="月份", y="食品烟酒类居民消费价格指数", data=df)
plt.show()
sns.lineplot(x="月份", y="衣着类居民消费价格指数", data=df)
plt.show()
sns.lineplot(x="月份", y="居住类居民消费价格指数", data=df)
plt.show()
sns.lineplot(x="月份", y="生活用品及服务类居民消费价格指数", data=df)
plt.show()
sns.lineplot(x="月份", y="交通和通信类居民消费价格指数", data=df)
plt.show()
sns.lineplot(x="月份", y="教育文化和娱乐类居民消费价格指数", data=df)
plt.show()
sns.lineplot(x="月份", y="医疗保健类居民消费价格指数", data=df)
plt.show()
sns.lineplot(x="月份", y="其他用品和服务类居民消费价格指数", data=df)
plt.show()
# 5.3 柱状图
# （1）matplotlib
plt.figure(figsize=(10, 15))
month_x = [i for i in range(1, 13)]
x_indexes = np.arange(len(data1))
width = 0.33
plt.bar(x_indexes, data1, width=width, label="居民消费价格指数")
plt.bar(x_indexes + width, data2, width=width, label="食品烟酒类居民消费价格指数")
plt.bar(x_indexes + width*2, data3, width=width, label="衣着类居民消费价格指数")
plt.xlabel("月份")
plt.ylabel("指数")
plt.title("月份和居民、食品烟酒类居民、衣着类居民消费价格指数的关系")
plt.legend()
plt.xticks(ticks=x_indexes, labels=month_x)
plt.legend(loc="best")
plt.show()
# （2）seaborn
sns.set_context({
'figure.figsize': [11, 7]})
sns.barplot(x="月份", y="食品烟酒类居民消费价格指数", data=df)
# 5.4 饼图
# （1）matplotlib
plt.figure(figsize=(8, 8))
data1_sum = sum(data1)
data2_sum = sum(data2)
data3_sum = sum(data3)
data4_sum = sum(data4)
data5_sum = sum(data5)
data6_sum = sum(data6)
data7_sum = sum(data7)
data8_sum = sum(data8)
data9_sum = sum(data9)
all_sum = sum([data1_sum, data2_sum, data3_sum, data4_sum, data5_sum, data6_sum, data7_sum,data8_sum, data9_sum])
data1_per = round(data1_sum / (all_sum/100), 2)
data2_per = round(data2_sum / (all_sum/100), 2)
data3_per = round(data3_sum / (all_sum/100), 2)
data4_per = round(data4_sum / (all_sum/100), 2)
data5_per = round(data5_sum / (all_sum/100), 2)
data6_per = round(data6_sum / (all_sum/100), 2)
data7_per = round(data7_sum / (all_sum/100), 2)
data8_per = round(data8_sum / (all_sum/100), 2)
data9_per = round(data9_sum / (all_sum/100), 2)
# print(data1_per+data2_per+data3_per+data4_per+data5_per+data6_per+data7_per+data8_per+data9_per)
size = [data1_per, data2_per, data3_per, data4_per, data5_per, data6_per, data7_per, data8_per, data9_per]
labels = ["居民", "食品烟酒类居民", "衣着类居民",
"居住类居民", "生活用品及服务类居民", "交通和通信类居民",
"教育文化和娱乐类居民", "医疗保健类居民", "其他用品和服务类居民"]
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "orange", "blue", "white", "pink", "red"]
explode = (0.1, 0, 0, 0, 0, 0, 0, 0, 0) # 突出显示第一个扇形
plt.pie(size, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90)
plt.title("2022年居民消费价格指数分布")
plt.show()
# （2） seaborn
colors = sns.color_palette('pastel')
explode = (0, 0, 0, 0.1, 0, 0, 0, 0, 0)
plt.pie(size, labels=labels, explode=explode, colors=colors, autopct="%1.2f%%")
plt.title("2022年各类居民消费价格指数分布")
plt.show()
# 5.5 箱线图
# （1） matplotlib
df1 = pd.DataFrame([data1, data2, data3, data4, data5, data6, data7, data8, data9], columns=['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'])
f = df1.boxplot(sym='o', # 异常点形状
vert=True, # 是否垂直
whis=1.5, # IQR
patch_artist=True, # 上下四分位框是否填充
meanline=False, showmeans=True, # 是否有均值线及其形状
showbox=True, # 是否显示箱线
showfliers=True, # 是否显示异常值
notch=False, # 中间箱体是否缺口
return_type='dict') # 返回类型为字典
plt.title('消费指数箱线图')
plt.show()
# （2）seaborn
box = sns.boxplot(data=df1)
box.set_title("2022年全年各类居民消费价格指数箱线图")
plt.show()
# 5.6 散点图
# （1）matplotlib
plt.figure(figsize=(8, 8)) # 设置画布大小
x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
y = np.array(data1)
plt.scatter(x, y, color='red', label='居民消费价格指数')
y = np.array(data2)
plt.scatter(x, y, color='green', label='食品烟酒类居民消费价格指数')
y = np.array(data3)
plt.scatter(x, y, color='yellow', label='衣着类居民消费价格指数')
y = np.array(data4)
plt.scatter(x, y, color='black', label='居住类居民消费价格指数')
y = np.array(data5)
plt.scatter(x, y, color='pink', label='生活用品及服务类居民消费价格指数')
y = np.array(data6)
plt.scatter(x, y, color='orange', label='交通和通信类居民消费价格指数')
y = np.array(data7)
plt.scatter(x, y, color='blue', label='教育文化和娱乐类居民消费价格指数')
y = np.array(data8)
plt.scatter(x, y, color='purple', label='医疗保健类居民消费价格指数')
y = np.array(data9)
plt.scatter(x, y, color='skyblue', label='其他用品和服务类居民消费价格指数')
plt.legend(loc='best', fontsize=10)
plt.xlabel("月份", fontdict={
'size': 12})
plt.ylabel("指数", fontdict={
'size': 12})
plt.title("居民各类消费指数散点图")
plt.show()
# （2）seaborn
data_plot = pd.DataFrame({
"月份": x, "指数": data1})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data2})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data3})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data4})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data5})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data6})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data7})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data8})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data9})
sc = sns.scatterplot(x="月份", y="指数", data=data_plot)
sc.set_title("2022年居民消费指数散点图")
plt.show()
# 5.7 回归图
# （1）matplotlib
# 绘制散点
plt.figure(figsize=(8, 8)) # 设置画布大小
x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
y = np.array(data1)
plt.scatter(x, y, color='red', label='居民消费价格指数')
y = np.array(data2)
plt.scatter(x, y, color='green', label='食品烟酒类居民消费价格指数')
y = np.array(data3)
plt.scatter(x, y, color='yellow', label='衣着类居民消费价格指数')
y = np.array(data4)
plt.scatter(x, y, color='black', label='居住类居民消费价格指数')
y = np.array(data5)
plt.scatter(x, y, color='pink', label='生活用品及服务类居民消费价格指数')
y = np.array(data6)
plt.scatter(x, y, color='orange', label='交通和通信类居民消费价格指数')
y = np.array(data7)
plt.scatter(x, y, color='blue', label='教育文化和娱乐类居民消费价格指数')
y = np.array(data8)
plt.scatter(x, y, color='purple', label='医疗保健类居民消费价格指数')
y = np.array(data9)
plt.scatter(x, y, color='skyblue', label='其他用品和服务类居民消费价格指数')
plt.legend(loc='best', fontsize=10)
plt.xlabel("月份", fontdict={
'size': 12})
plt.ylabel("指数", fontdict={
'size': 12})
plt.title("居民各类消费指数回归图")
# 绘制回归线
x_mean = np.mean(month_x)
y_mean = np.mean(data3)
m1 = 0 # 分母
m2 = 0 # 分子
for x_i, y_i in zip(month_x, data3):
m1 += (x_i - x_mean) * (y_i - y_mean)
m2 += (x_i - x_mean) ** 2
a = m1 / m2
b = y_mean - a * x_mean
print(a, b)
y_line = [a * x + b for x in data3]
print(x)
print(y_line)
plt.plot([1, 12], [99.9, 102.8], color="black", linewidth=1, label="回归线")
plt.axis([1, 12, 98, 108])
plt.show()
# （2）seaborn
data_plot = pd.DataFrame({
"月份": x, "指数": data1})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data2})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data3})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data4})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data5})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data6})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data7})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data8})
sns.scatterplot(x="月份", y="指数", data=data_plot)
data_plot = pd.DataFrame({
"月份": x, "指数": data9})
sns.scatterplot(x="月份", y="指数", data=data_plot)
sns.regplot(x="月份", y="指数", data=data_plot)
sc.set_title("2022年居民消费指数回归图")
plt.show()
# 均值、方差、标准差：
# https://blog.csdn.net/robert_chen1988/article/details/102712946
# 中位数的计算：https://blog.csdn.net/Yiess/article/details/121102021
# 四分位数：https://blog.csdn.net/Changxing_J/article/details/106232995
# 极差、四分位差、方差、标准差、变异系数：https://blog.csdn.net/walking_visitor/article/details/83503008
# 方差、标准差：https://blog.csdn.net/Louise_Trender/article/details/105828562
# 变异系数：https://blog.csdn.net/Changxing_J/article/details/106233469
# matplotlib: https://matplotlib.org/devdocs/gallery/lines_bars_and_markers/barchart.html#sphx-glr-gallery-lines-bars-and-markers-barchart-py
# 绘图：https://blog.csdn.net/m0_56521890/article/details/129224416
# 直方图：https://blog.csdn.net/weixin_44327634/article/details/123745262
2. Iris data set
1. Data sources
The data source website used: tensorflow.org
data website link: http://download.tensorflow.org/data/iris_training.csv
2. Download data
Just click on the web link to download.
The meaning of the data column:
3. Central tendency analysis: mean, median, upper quartile, lower quartile
5. Calculate range, quartile deviation, variance, standard deviation, coefficient of variation (standard deviation/mean)
6. Use Matplotlib to draw histograms, line charts, histograms (bar charts), pie charts, box plots, scatter plots, and regression charts
1. Histogram
2. Line chart
3. Histogram (bar graph)
4. Pie chart
5. Boxplot
A boxplot contains a set of data: median, upper quartile, lower quartile, inner limit, outer limit, outliers.
6. Scatterplot
7. Regression graph
7. Use Seaborn to draw histograms, line charts, histograms (bar charts), pie charts, box plots, scatter plots, and regression charts
1. Histogram
2. Line chart
3. Histogram (bar graph)
4. Pie chart
5. Boxplot
6. Scatterplot
7. Regression graph
8. Source code
import pandas as pd
import math
import numpy as np
from collections import Counter, defaultdict
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
def get_average(data_list): # 得到平均值
data_length = len(data_list)
data_sum = sum(data_list)
return round(data_sum/data_length, 2) # 保留两位小数
def get_median(data_list): # 计算中位数
data_list.sort()
list_length = len(data_list)
if list_length % 2 == 0:
return (data_list[int(list_length / 2) - 1] + data_list[int(list_length / 2)]) / 2
return data_list[int(list_length / 2)]
def get_quartile(data_list, x): # 计算四分位数 # 1：下四分位数 # 3：上四分位数
if x < 1 or x > 3:
return False
data_list.sort() # 给数据排序
position = (len(data_list) + 1)*x/4
pos_integer = int(math.modf(position)[1])
pos_decimal = position - pos_integer
quartile = data_list[pos_integer - 1] + (data_list[pos_integer] - data_list[pos_integer - 1])*pos_decimal
return quartile
def get_range(data_max, data_min): # 获得极差、四分位差
return round(data_max - data_min, 2)
def get_variance(data_list): # 获得方差
return round(np.var(data_list), 2)
def get_std(data_list): # 得到标准差
return round(np.std(data_list, ddof=1), 2)
def get_coefficient(data_std, data_mean): # 获得变异系数 标准差/均值
return round(data_std / data_mean, 2)
def plt_picture_hist(data, name): # matplotlib绘制直方图
plt.hist(data, bins=10, edgecolor="white", label=name)
plt.title(f'鸢尾花{
name}分布直方图')
plt.xlabel('长度(单位：cm)')
plt.ylabel('数量')
plt.tight_layout()
plt.show()
def sns_picture_hist(data, name, rc): # seaborn绘制直方图
sns.set(context="notebook", style="ticks", rc=rc)
dis = sns.distplot(data, bins=10, kde=False, rug=True)
dis.set_title(f'鸢尾花{
name}分布直方图')
dis.set_xlabel("长度（单位: cm）")
dis.set_ylabel("数量")
plt.show()
def plt_picture_plot(x1, y1, x2, y2): # 绘制折线图
# x 是长度，y是宽度
plt.xlabel("长度")
plt.ylabel("宽度")
plt.title("鸢尾花长宽折线图")
plt.plot(x1, y1, label="萼片")
plt.plot(x2, y2, label="花瓣")
plt.legend(loc="best")
plt.show()
def main():
# 获取数据
columns = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
df_iris = pd.read_csv("./iris_training.csv", header=0, names=columns)
# print(df_iris)
SepalLength = list(df_iris['SepalLength']) # 取出指定列，萼片长度
SepalWidth = list(df_iris['SepalWidth']) # 取出指定列， 萼片宽度
PetalLength = list(df_iris['PetalLength']) # 取出指定列，花瓣长度
PetalWidth = list(df_iris['PetalWidth']) # 取出指定列， 花瓣宽度
Species = list(df_iris['Species']) # 取出种类
# ===================================================================
# 1. 平均值
mean1 = get_average(SepalLength)
print("萼片长度平均值：", mean1, "cm")
mean2 = get_average(SepalWidth)
print("萼片宽度平均值：", mean2, "cm")
mean3 = get_average(PetalLength)
print("花瓣长度平均值：", mean3, "cm")
mean4 = get_average(PetalWidth)
print("花瓣宽度平均值：", mean4, "cm")
print()
# 2. 中位数
print("萼片长度中位数：", get_median(SepalLength), "cm")
print("萼片宽度中位数：", get_median(SepalWidth), "cm")
print("花瓣长度中位数：", get_median(PetalLength), "cm")
print("花瓣宽度中位数：", get_median(PetalWidth), "cm")
print()
# 3. 上四分位数
up1 = get_quartile(SepalLength, 3)
print("萼片长度上四分位数：", up1, "cm")
up2 = get_quartile(SepalWidth, 3)
print("萼片宽度上四分位数：", up2, "cm")
up3 = get_quartile(PetalLength, 3)
print("花瓣长度上四分位数：", up3, "cm")
up4 = get_quartile(PetalWidth, 3)
print("花瓣宽度上四分位数：", up4, "cm")
print()
# 4. 下四分位数
down1 = get_quartile(SepalLength, 1)
print("萼片长度下四分位数：", down1, "cm")
down2 = get_quartile(SepalWidth, 1)
print("萼片宽度下四分位数：", down2, "cm")
down3 = get_quartile(PetalLength, 1)
print("花瓣长度下四分位数：", down3, "cm")
down4 = get_quartile(PetalWidth, 1)
print("花瓣宽度下四分位数：", down4, "cm")
print()
# ===================================================================
# 1. 极差
print("萼片长度极差：", get_range(max(SepalLength), min(SepalLength)), "cm")
print("萼片宽度极差：", get_range(max(SepalWidth), min(SepalWidth)), "cm")
print("花瓣长度极差：", get_range(max(PetalLength), min(PetalLength)), "cm")
print("花瓣宽度极差：", get_range(max(PetalWidth), min(PetalWidth)), "cm")
print()
# 2. 四分位差
print("萼片长度四分位差：", get_range(up1, down1), "cm")
print("萼片宽度四分位差：", get_range(up2, down2), "cm")
print("花瓣长度四分位差：", get_range(up3, down3), "cm")
print("花瓣宽度四分位差：", get_range(up4, down4), "cm")
print()
# 3. 方差
print("萼片长度方差：", get_variance(SepalLength))
print("萼片宽度方差：", get_variance(SepalWidth))
print("花瓣长度方差：", get_variance(PetalLength))
print("花瓣宽度方差：", get_variance(PetalWidth))
print()
# 4. 标准差
std1 = get_std(SepalLength)
print("萼片长度标准差：", std1)
std2 = get_std(SepalWidth)
print("萼片宽度标准差：", std2)
std3 = get_std(PetalLength)
print("花瓣长度标准差：", std3)
std4 = get_std(PetalWidth)
print("花瓣宽度标准差：", std4)
print()
# 5. 变异系数
print("萼片长度变异系数：", get_coefficient(std1, mean1))
print("萼片宽度变异系数：", get_coefficient(std2, mean2))
print("花瓣长度变异系数：", get_coefficient(std3, mean3))
print("花瓣宽度变异系数：", get_coefficient(std4, mean4))
print()
# ===================================================================
matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 正常显示中文
rc = {
'font.sans-serif': 'SimHei',
'axes.unicode_minus': False}
# 1. 直方图
# matplotlib
plt_picture_hist(SepalLength, "萼片长度")
plt_picture_hist(SepalWidth, "萼片宽度")
plt_picture_hist(PetalLength, "花瓣长度")
plt_picture_hist(PetalWidth, "花瓣宽度")
# seaborn
sns_picture_hist(SepalLength, "萼片长度", rc)
sns_picture_hist(SepalWidth, "萼片宽度", rc)
sns_picture_hist(PetalLength, "花瓣长度", rc)
sns_picture_hist(PetalWidth, "花瓣宽度", rc)
# 2. 折线图
# matplotlib
plt_picture_plot(SepalLength, SepalWidth, PetalLength, PetalWidth)
# seaborn
lin1=sns.lineplot(x="SepalLength", y="SepalWidth", data=df_iris)
lin1.set_title("鸢尾花萼片长度和宽度折线图")
plt.show()
lin2 = sns.lineplot(x="PetalLength", y="PetalWidth", data=df_iris)
lin2.set_title("鸢尾花花瓣长度和宽度折线图")
plt.show()
# 3.柱状图
# matplotlib
plt.bar(PetalLength, PetalWidth, edgecolor="white")
plt.title("鸢尾花花瓣长度宽度柱状图")
plt.xlabel("长度")
plt.ylabel("宽度")
plt.show()
# seaborn
sns.set_context({
'figure.figsize': [11, 7]})
bar = sns.barplot(x='PetalLength', y='PetalWidth', data=df_iris) # 条形图
bar.set_title("鸢尾花花瓣长度宽度柱状图")
plt.show()
# 4. 饼图
# matplotlib
setosa_0 = Species.count(0)
versicolor_1 = Species.count(1)
virginica_2 = Species.count(2)
count_sum = setosa_0 + versicolor_1 + virginica_2
sizes = [setosa_0/count_sum, versicolor_1/count_sum, virginica_2/count_sum]
labels = ["山鸢尾", "变色鸢尾", "维吉尼亚鸢尾"]
colors = ["pink", "purple", "skyblue"]
explode = (0.1, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=60)
plt.title("鸢尾花类型分布")
plt.show()
# seaborn
colors = sns.color_palette('pastel')
explode = (0, 0, 0.1)
plt.pie(sizes, labels=labels, explode=explode, colors=colors, autopct="%1.2f%%")
plt.title("鸢尾花类型分布")
plt.show()
# 5. 箱线图
# matplotlib
df_iris.boxplot(sym='o', # 异常点形状
vert=True, # 是否垂直
whis=1.5, # IQR
patch_artist=True, # 上下四分位框是否填充
meanline=False, showmeans=True, # 是否有均值线及其形状
showbox=True, # 是否显示箱线
showfliers=True, # 是否显示异常值
notch=False, # 中间箱体是否缺口
return_type='dict') # 返回类型为字典
plt.ylabel("长度/(cm)")
plt.xlabel("指标")
plt.title('鸢尾花各项指标箱线图')
plt.show()
# seaborn
sns.boxplot(x=df_iris["Species"], y=df_iris['SepalWidth'])
plt.title("鸢尾花类别和花瓣宽度箱线图")
plt.show()
# 6. 散点图
# matplotlib
plt.title("鸢尾花萼片和花瓣长宽散点图")
plt.scatter(SepalLength, SepalWidth, label='萼片')
plt.scatter(PetalLength, PetalWidth, label='花瓣')
plt.xlabel("长度/(cm)")
plt.ylabel("宽度/(cm)")
plt.legend(loc="best")
plt.show()
# seaborn
sns.scatterplot(x="SepalLength", y="SepalWidth", data=df_iris)
sc = sns.scatterplot(x="PetalLength", y="PetalWidth", data=df_iris)
plt.xlabel("长度")
plt.ylabel("宽度")
sc.set_title("鸢尾花花瓣萼片长宽散点图")
plt.show()
# 7. 回归图
# matplotlib
# 散点
plt.title("鸢尾花萼片和花瓣长宽回归图")
plt.scatter(SepalLength, SepalWidth, label='萼片')
plt.scatter(PetalLength, PetalWidth, label='花瓣')
plt.xlabel("长度/(cm)")
plt.ylabel("宽度/(cm)")
plt.legend(loc="best")
# 回归线
plt.plot([1, 8], [0, 4.5], color="black", linewidth=1, label="回归线")
plt.show()
# seaborn
# 散点
sns.scatterplot(x="SepalLength", y="SepalWidth", data=df_iris)
sc = sns.scatterplot(x="PetalLength", y="PetalWidth", data=df_iris)
sc.set_title("鸢尾花花瓣萼片长宽回归图")
# 回归线
sns.regplot(x="SepalLength", y="SepalWidth", data=df_iris)
plt.xlabel("长度")
plt.ylabel("宽度")
plt.show()
if __name__ == "__main__":
main()
# 参考：https://blog.csdn.net/qinzhongyuan/article/details/106434854
# 选取指定行或列：https://zhuanlan.zhihu.com/p/76241647