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數據集:大學畢業生收入

Aklman / 2106人閱讀

摘要:數據集大學畢業生收入下載地址,本文以繪制直方圖為主。整型全年全職在崗人數。浮點型收入的百分位數。各大類專業就業率圖示結論相對來說,由于計算機的發展前景,計算機與數學類的就業率較高。

數據集:大學畢業生收入

下載地址,本文以繪制直方圖為主。

1. 字段描述

字段名稱字段類型字段說明
Major_code整型專業代碼。
Major字符型專業名稱。
Major_category字符型專業所屬目錄。
Total整型總人數。
Employed整型就業人數。
Employed_full_time_year_round整型全年全職在崗人數。
Unemployed整型失業人數。
Unemployment_rate浮點型失業率。
Median整型收入的中位數。
P25th整型收入的25百分位數。
P75th浮點型收入的75百分位數。

2. 數據預處理

2.1 導包

import numpy as npimport matplotlib.pyplot as pltimport pandas as pdimport osimport warningswarnings.filterwarnings("ignore")

2.2 讀取數據

df = pd.read_csv("大學畢業生收入數據集.csv")

3. 數據預覽

3.1 預覽數據

print(df.head())

結果

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  90000.0

3.2 查看基本信息

df.info()

結果

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)

3.3 查看重復值

print(df.duplicated().sum())

結果

0

3.4 查看缺失值

print(df.isnull().sum())

結果

Major_code                       0Major                            0Major_category                   0Total                            0Employed                         0Employed_full_time_year_round    0Unemployed                       0Unemployment_rate                0Median                           0P25th                            0P75th                            0dtype: int64

4. 數據集描述性信息

describe = df.describe()print(describe)

結果

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

5. 數據分析

5.1 各專業種類(Major_category)的專業分支個數

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

結論
由于機械類專業發展歷史悠久,故相對來說機械類專業分支數相較其他大類專業要多

5.2 各大類專業收入

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

結論
由于機械類專業與人工智能、自動化等領域相關,故平均工資比較高;計算機與數學類專業發展前景很好,但是小公司工資普遍不高,大公司工資相對來說較高。

5.3 各大類專業失業率

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

結論
藝術類專業由于可變動性特別大,加上對人才的要求相對來說較為苛刻,故失業率較高。

5.4 各大類專業就業率

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

結論
相對來說,由于計算機的發展前景,計算機與數學類的就業率較高。

5.5 各大類專業全年全職在崗率

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

5.6 各大類專業總人數

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

5.7 就業失業比

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

結論
相對來說,農業就業的門檻低,就業率高的同時失業率低。

6. 完整代碼

# 導包import numpy as npimport matplotlib.pyplot as pltimport pandas as pdimport osimport warningswarnings.filterwarnings("ignore")# 讀取數據df = pd.read_csv("大學畢業生收入數據集.csv")# 預覽數據print(df.head())# 規范字段名稱(本數據集已經較為規范)# 查看基本信息df.info()# 查看重復值print(df.duplicated().sum())# 查看缺失值print(df.isnull().sum())# 查看數據集描述性信息describe = df.describe()print(describe)# 統計表中每個專業種類(Major_category)的個數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()# 對各大類專業收入作統計并作圖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()# 對各大類專業失業率作統計并作圖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()# 對各大類專業就業率作統計并作圖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()# 對各大類專業全年全職在崗率作統計并作圖(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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