資訊專欄INFORMATION COLUMN
摘要：數(shù)據(jù)集大學(xué)畢業(yè)生收入下載地址，本文以繪制直方圖為主。整型全年全職在崗人數(shù)。浮點(diǎn)型收入的百分位數(shù)。各大類專業(yè)就業(yè)率圖示結(jié)論相對來說，由于計(jì)算機(jī)的發(fā)展前景，計(jì)算機(jī)與數(shù)學(xué)類的就業(yè)率較高。
下載地址，本文以繪制直方圖為主。
| 字段名稱 | 字段類型 | 字段說明 |
|---|---|---|
| Major_code | 整型 | 專業(yè)代碼。 |
| Major | 字符型 | 專業(yè)名稱。 |
| Major_category | 字符型 | 專業(yè)所屬目錄。 |
| Total | 整型 | 總?cè)藬?shù)。 |
| Employed | 整型 | 就業(yè)人數(shù)。 |
| Employed_full_time_year_round | 整型 | 全年全職在崗人數(shù)。 |
| Unemployed | 整型 | 失業(yè)人數(shù)。 |
| Unemployment_rate | 浮點(diǎn)型 | 失業(yè)率。 |
| Median | 整型 | 收入的中位數(shù)。 |
| P25th | 整型 | 收入的25百分位數(shù)。 |
| P75th | 浮點(diǎn)型 | 收入的75百分位數(shù)。 |
import numpy as npimport matplotlib.pyplot as pltimport pandas as pdimport osimport warningswarnings.filterwarnings("ignore")df = pd.read_csv("大學(xué)畢業(yè)生收入數(shù)據(jù)集.csv")print(df.head())結(jié)果：
Major_code Major ... P25th P75th0 1100 GENERAL AGRICULTURE ... 34000 80000.01 1101 AGRICULTURE PRODUCTION AND MANAGEMENT ... 36000 80000.02 1102 AGRICULTURAL ECONOMICS ... 40000 98000.03 1103 ANIMAL SCIENCES ... 30000 72000.04 1104 FOOD SCIENCE ... 38500 90000.0df.info()結(jié)果：
RangeIndex: 173 entries, 0 to 172Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Major_code 173 non-null int64 1 Major 173 non-null object 2 Major_category 173 non-null object 3 Total 173 non-null int64 4 Employed 173 non-null int64 5 Employed_full_time_year_round 173 non-null int64 6 Unemployed 173 non-null int64 7 Unemployment_rate 173 non-null float64 8 Median 173 non-null int64 9 P25th 173 non-null int64 10 P75th 173 non-null float64dtypes: float64(2), int64(7), object(2)print(df.duplicated().sum())結(jié)果：
0print(df.isnull().sum())結(jié)果：
Major_code 0Major 0Major_category 0Total 0Employed 0Employed_full_time_year_round 0Unemployed 0Unemployment_rate 0Median 0P25th 0P75th 0dtype: int64describe = df.describe()print(describe)結(jié)果：
Major_code Total ... P25th P75thcount 173.000000 1.730000e+02 ... 173.000000 173.000000mean 3879.815029 2.302566e+05 ... 38697.109827 82506.358382std 1687.753140 4.220685e+05 ... 9414.524761 20805.330126min 1100.000000 2.396000e+03 ... 24900.000000 45800.00000025% 2403.000000 2.428000e+04 ... 32000.000000 70000.00000050% 3608.000000 7.579100e+04 ... 36000.000000 80000.00000075% 5503.000000 2.057630e+05 ... 42000.000000 95000.000000max 6403.000000 3.123510e+06 ... 78000.000000 210000.000000[8 rows x 9 columns]可在變量視圖中查看
describe
Major_category_counts=df["Major_category"].value_counts()print(Major_category_counts)rects = plt.bar(range(1,17),Major_category_counts);for rect in rects: #rects 是三根柱子的集合 height = rect.get_height() plt.text(rect.get_x() + rect.get_width() / 2, height, str(height), size=12, ha="center", va="bottom")interval = ["Engineering","Education","Humanities & Liberal Arts","Biology & Life Science","Business","Health","Computers & Mathematics","Agriculture & Natural Resources","Physical Sciences","Social Science","Psychology & Social Work","Arts","Industrial Arts & Consumer Services","Law & Public Policy","Communications & Journalism","Interdisciplinary"]plt.xticks(range(1,17),interval,rotation=90);plt.title("Number of Branches by Major Category")plt.ylabel("Counts")plt.show()結(jié)果：
Engineering 29Education 16Humanities & Liberal Arts 15Biology & Life Science 14Business 13Health 12Computers & Mathematics 11Agriculture & Natural Resources 10Physical Sciences 10Social Science 9Psychology & Social Work 9Arts 8Industrial Arts & Consumer Services 7Law & Public Policy 5Communications & Journalism 4Interdisciplinary 1Name: Major_category, dtype: int64圖示：
結(jié)論：
由于機(jī)械類專業(yè)發(fā)展歷史悠久，故相對來說機(jī)械類專業(yè)分支數(shù)相較其他大類專業(yè)要多
averageMoney = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Median"][j] averageMoney.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageMoney);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Annual salary by Major Category")plt.ylabel("Moneys")plt.show()圖示：
結(jié)論：
由于機(jī)械類專業(yè)與人工智能、自動(dòng)化等領(lǐng)域相關(guān)，故平均工資比較高；計(jì)算機(jī)與數(shù)學(xué)類專業(yè)發(fā)展前景很好，但是小公司工資普遍不高，大公司工資相對來說較高。
averageUnemployRate = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Unemployment_rate"][j] averageUnemployRate.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageUnemployRate);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Unemployment Rate by Major Category")plt.ylabel("Rate")plt.show()圖示：
結(jié)論：
藝術(shù)類專業(yè)由于可變動(dòng)性特別大，加上對人才的要求相對來說較為苛刻，故失業(yè)率較高。
averageEmployRate = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Employed"][j] / df["Total"][j] averageEmployRate.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageEmployRate);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Employment Rate by Major Category")plt.ylabel("Rate")plt.show()圖示：
結(jié)論：
相對來說，由于計(jì)算機(jī)的發(fā)展前景，計(jì)算機(jī)與數(shù)學(xué)類的就業(yè)率較高。
averageFullTimeRate = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Employed_full_time_year_round"][j] / df["Employed"][j] averageFullTimeRate.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageFullTimeRate);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Full-Time Rate by Major Category")plt.ylabel("Rate")plt.show()圖示：
averageNum = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Total"][j] averageNum.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageNum);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Total Numbers by Major Category")plt.ylabel("Counts")plt.show()圖示：
EUratio = []for i in range(len(interval)): EUratio.append(averageEmployRate[i]/averageUnemployRate[i])plt.bar(range(1,17),EUratio);plt.xticks(range(1,17),interval,rotation=90);plt.title("Employment-Unemployment Ratio by Major Category")plt.ylabel("Ratio")plt.show()圖示：
結(jié)論：
相對來說，農(nóng)業(yè)就業(yè)的門檻低，就業(yè)率高的同時(shí)失業(yè)率低。
# 導(dǎo)包import numpy as npimport matplotlib.pyplot as pltimport pandas as pdimport osimport warningswarnings.filterwarnings("ignore")# 讀取數(shù)據(jù)df = pd.read_csv("大學(xué)畢業(yè)生收入數(shù)據(jù)集.csv")# 預(yù)覽數(shù)據(jù)print(df.head())# 規(guī)范字段名稱(本數(shù)據(jù)集已經(jīng)較為規(guī)范)# 查看基本信息df.info()# 查看重復(fù)值print(df.duplicated().sum())# 查看缺失值print(df.isnull().sum())# 查看數(shù)據(jù)集描述性信息describe = df.describe()print(describe)# 統(tǒng)計(jì)表中每個(gè)專業(yè)種類（Major_category）的個(gè)數(shù)Major_category_counts=df["Major_category"].value_counts()print(Major_category_counts)rects = plt.bar(range(1,17),Major_category_counts);for rect in rects: #rects 是三根柱子的集合 height = rect.get_height() plt.text(rect.get_x() + rect.get_width() / 2, height, str(height), size=12, ha="center", va="bottom")interval = ["Engineering","Education","Humanities & Liberal Arts","Biology & Life Science","Business","Health","Computers & Mathematics","Agriculture & Natural Resources","Physical Sciences","Social Science","Psychology & Social Work","Arts","Industrial Arts & Consumer Services","Law & Public Policy","Communications & Journalism","Interdisciplinary"]plt.xticks(range(1,17),interval,rotation=90);plt.title("Number of Branches by Major Category")plt.ylabel("Counts")plt.show()# 對各大類專業(yè)收入作統(tǒng)計(jì)并作圖averageMoney = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Median"][j] averageMoney.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageMoney);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Annual salary by Major Category")plt.ylabel("Moneys")plt.show()# 對各大類專業(yè)失業(yè)率作統(tǒng)計(jì)并作圖averageUnemployRate = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Unemployment_rate"][j] averageUnemployRate.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageUnemployRate);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Unemployment Rate by Major Category")plt.ylabel("Rate")plt.show()# 對各大類專業(yè)就業(yè)率作統(tǒng)計(jì)并作圖averageEmployRate = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Employed"][j] / df["Total"][j] averageEmployRate.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageEmployRate);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Employment Rate by Major Category")plt.ylabel("Rate")plt.show()# 對各大類專業(yè)全年全職在崗率作統(tǒng)計(jì)并作圖（沒有早退的）averageFullTimeRate = []for i in range(len(interval)): sum = 0 for j in range(173): if df["Major_category"][j] == interval[i]: sum = sum + df["Employed_full_time_year_round"][j] / df["Employed"][j] averageFullTimeRate.append(sum/Major_category_counts[i])plt.bar(range(1,17),averageFullTimeRate);plt.xticks(range(1,17),interval,rotation=90);plt.title("Average Full-Time Rate
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