python数据分析之员工个人信息可视化_Python

一、实验目的

（1）熟练使用Counter类进行统计
（2）掌握pandas中的cut方法进行分类
（3）掌握matplotlib第三方库，能熟练使用该三方库库绘制图形

二、实验内容

采集到的数据集如下表格所示：

python数据分析之员工个人信息可视化

三、实验要求

1.按照性别进行分类，然后分别汇总男生和女生总的收入，并用直方图进行展示。

2.男生和女生各占公司总人数的比例，并用扇形图进行展示。

3.按照年龄进行分类（20-29岁，30-39岁，40-49岁）,然后统计出各个年龄段有多少人，并用直方图进行展示。

import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter

info = [{"name": "E001", "gender": "man", "age": "34", "sales": "123", "income": 350},
        {"name": "E002", "gender": "feman", "age": "40", "sales": "114", "income": 450},
        {"name": "E003", "gender": "feman", "age": "37", "sales": "135", "income": 169},
        {"name": "E004", "gender": "man", "age": "30", "sales": "139", "income": 189},
        {"name": "E005", "gender": "feman", "age": "44", "sales": "117", "income": 183},
        {"name": "E006", "gender": "man", "age": "36", "sales": "121", "income": 80},
        {"name": "E007", "gender": "man", "age": "32", "sales": "133", "income": 166},
        {"name": "E008", "gender": "feman", "age": "26", "sales": "140", "income": 120},
        {"name": "E009", "gender": "man", "age": "32", "sales": "133", "income": 75},
        {"name": "E010", "gender": "man", "age": "36", "sales": "133", "income": 40}
        ]


# 读取数据
def get_data():
    df = pd.DataFrame(info)#DataFrame是一个以命名列方式组织的分布式数据集
    df[["age"]] = df[["age"]].astype(int)  # 数据类型转为int
    df[["sales"]] = df[["sales"]].astype(int)  # 数据类型转为int
    return df


def group_by_gender(df):
    var = df.groupby("gender").sales.sum()#groupby将元素通过函数生成相应的Key,数据就转化为Key-Value格式,之后将Key相同的元素分为一组
    fig = plt.figure()
    ax1 = fig.add_subplot(211)#2*1个网格，1个子图
    ax1.set_xlabel("Gender")  # x轴标签
    ax1.set_ylabel("Sum of Sales")  # y轴标签
    ax1.set_title("Gender wise Sum of Sales")  # 设置图标标题
    var.plot(kind="bar")
    plt.show()  # 显示


def group_by_age(df):
    age_list = [20, 30, 40, 50]
    res = pd.cut(df["age"], age_list, right=False)
    count_res = pd.value_counts(res)
    df_count_res = pd.DataFrame(count_res)
    print(df_count_res)
    plt.hist(df["age"], bins=age_list, alpha=0.7)  # age_list 根据年龄段统计
    # 显示横轴标签
    plt.xlabel("nums")
    # 显示纵轴标签
    plt.ylabel("ages")
    # 显示图标题
    plt.title("pic")
    plt.show()


def gender_count(df):
    res = df["gender"].value_counts()
    df_res = pd.DataFrame(res)
    label_list = df_res.index

    plt.axis("equal")
    plt.pie(df_res["gender"], labels=label_list,
            autopct="%1.1f%%",
            shadow=True,  # 设置阴影
            explode=[0, 0.1])  # 0 ：扇形不分离，0.1：分离0.1单位
    plt.title("gender ratio")
    plt.show()

    print(df_res)
    print(label_list)


if __name__ == "__main__":
    data = get_data()
    group_by_gender(data)
    gender_count(data)
    group_by_age(data)

python数据分析之员工个人信息可视化