一、實驗目的
（1）熟練使用Counter類進行統(tǒng)計
（2）掌握pandas中的cut方法進行分類
（3）掌握matplotlib第三方庫，能熟練使用該三方庫庫繪制圖形
二、實驗內(nèi)容
采集到的數(shù)據(jù)集如下表格所示：
三、實驗要求
1.按照性別進行分類，然后分別匯總男生和女生總的收入，并用直方圖進行展示。
2.男生和女生各占公司總人數(shù)的比例，并用扇形圖進行展示。
3.按照年齡進行分類（20-29歲，30-39歲，40-49歲）,然后統(tǒng)計出各個年齡段有多少人，并用直方圖進行展示。
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}
]
# 讀取數(shù)據(jù)
def get_data():
df = pd.DataFrame(info)#DataFrame是一個以命名列方式組織的分布式數(shù)據(jù)集
df[["age"]] = df[["age"]].astype(int) # 數(shù)據(jù)類型轉(zhuǎn)為int
df[["sales"]] = df[["sales"]].astype(int) # 數(shù)據(jù)類型轉(zhuǎn)為int
return df
def group_by_gender(df):
var = df.groupby("gender").sales.sum()#groupby將元素通過函數(shù)生成相應的Key,數(shù)據(jù)就轉(zhuǎn)化為Key-Value格式,之后將Key相同的元素分為一組
fig = plt.figure()
ax1 = fig.add_subplot(211)#2*1個網(wǎng)格，1個子圖
ax1.set_xlabel("Gender") # x軸標簽
ax1.set_ylabel("Sum of Sales") # y軸標簽
ax1.set_title("Gender wise Sum of Sales") # 設置圖標標題
var.plot(kind="bar")
plt.show() # 顯示
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 根據(jù)年齡段統(tǒng)計
# 顯示橫軸標簽
plt.xlabel("nums")
# 顯示縱軸標簽
plt.ylabel("ages")
# 顯示圖標題
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, # 設置陰影
explode=[0, 0.1]) # 0 ：扇形不分離，0.1：分離0.1單位
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)
到此這篇關于python數(shù)據(jù)分析之員工個人信息可視化的文章就介紹到這了,更多相關python員工信息可視化內(nèi)容請搜索服務器之家以前的文章或繼續(xù)瀏覽下面的相關文章希望大家以后多多支持服務器之家！
原文鏈接：https://blog.csdn.net/liarfeelings/article/details/116013798
