Python数据分析——白葡萄酒实训_分别用numpy和pandas改写“白葡萄酒品质探索”

一：数据收集

完整数据
实训之前我们需要先下载数据，我这里是下载完之后文件名是：white_wine.csv

二：读取数据

1：显示前5行数据

import csv

f = open("white_wine.csv",'r')
reader = csv.reader(f)

data = []
for row in reader:
    data.append(row)

for i in range(5):
    print(data[i])
f.close()
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三：数据处理

1：查看白葡萄酒总共分为几种品质等级

import csv

f = open("white_wine.csv",'r')
reader = csv.reader(f)

data = []
for row in reader:
    data.append(row)

quality_list = []
for row in data[1:]:
    quality_list.append(int(row[ -1]))

quality_count = set(quality_list)

print("白葡萄酒共有%s种等级, 分别为：%r"
      %(len(quality_count), quality_count))

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白葡萄酒共有7种等级, 分别为：{3, 4, 5, 6, 7, 8, 9}
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2：统计等级及其数量

import csv

f = open("white_wine.csv",'r')
reader = csv.reader(f)

data = []
for row in reader:
    data.append(row)


content_dict = {}
for row in data[1:]:
    quality = int(row[-1])
    if quality not in content_dict.keys():
        content_dict[quality] = [row]
    else:
        content_dict[quality].append(row)

for key in content_dict:
    print('等级为%d， 数量为%d' %(key, len(content_dict[key])))

f.close()

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等级为6， 数量为1539
等级为5， 数量为1020
等级为7， 数量为616
等级为8， 数量为123
等级为4， 数量为115
等级为3， 数量为14
等级为9， 数量为4
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条形图展示

import csv
import numpy as np
import matplotlib.pyplot as plt

f = open("white_wine.csv",'r')
reader = csv.reader(f)

data = []
for row in reader:
    data.append(row)


content_dict = {}
for row in data[1:]:
    quality = int(row[-1])
    if quality not in content_dict.keys():
        content_dict[quality] = [row]
    else:
        content_dict[quality].append(row)

x = []
y = []
for key in content_dict:
    x.append(key)
    y.append(len(content_dict[key]))

plt.bar(x, y)
plt.show()
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3：计算每个数据集中fixed acidity的均值

import csv

f = open("white_wine.csv",'r')
reader = csv.reader(f)

data = []
for row in reader:
    data.append(row)


content_dict = {}
for row in data[1:]:
    quality = int(row[-1])
    if quality not in content_dict.keys():
        content_dict[quality] = [row]
    else:
        content_dict[quality].append(row)

mean_list = []
for key,value in content_dict.items():
    sum = 0
    for row in value:
        sum += float(row[0])

    mean_list.append((key, sum / len(value)))
for item in mean_list:
    print(item[0],",", item[1])

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6 : 6.812085769980511
5 : 6.907843137254891
7 : 6.755844155844158
8 : 6.708130081300811
4 : 7.052173913043476
3 : 7.535714285714286
9 : 7.5
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