The method of exporting nested lists of dictionaries and nested dictionaries to csv

Dictionary nested list export csv

{'rank': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24 ', '25', '26', '27', '28', '29', '30'], 'school_name': ['Tsinghua University', 'Peking University', 'Zhejiang University', 'Shanghai Communications University', 'Fudan University', 'Nanjing University', 'University of Science and Technology of China', 'Huazhong University of Science and Technology', 'Wuhan University', 'Xi'an Jiaotong University', 'Sichuan University', 'Sun Yat-Sen University', 'Harbin Industry Universities', 'Tongji University', 'Beijing University of Aeronautics and Astronautics', 'Southeast University', 'Beijing Normal University', 'Beijing Institute of Technology', 'Renmin University of China', 'Nankai University', 'Tianjin University', 'Shandong University' University', 'Central South University','Northwestern Polytechnical University', 'South China University of Technology', 'Xiamen University', 'Jilin University', 'East China Normal University', 'China Agricultural University', 'University of Electronic Science and Technology'], 'school_position': ['Beijing', 'Beijing', 'Zhejiang', 'Shanghai', 'Shanghai', 'Jiangsu', 'Anhui', 'Hubei', 'Hubei', 'Shaanxi', 'Sichuan', 'Guangdong', 'Heilongjiang', 'Shanghai ', 'Beijing', 'Jiangsu', 'Beijing', 'Beijing', 'Beijing', 'Tianjin', 'Tianjin', 'Shandong', 'Hunan', 'Shaanxi', 'Guangdong', 'Fujian', 'Jilin', 'Shanghai', 'Beijing', 'Sichuan'], 'school_sum': ['999.4', '912.5', '825.3', '783.3', '697.8', '683.4', '609.9', '609.3', '607.1', '570.2', '561.7', '559.4', '549.8', '548.4', '546.7','538.5', '534.7', '530.9', '514.7', '497.4', '490.8', '484.3', '475.8', '467.9', '466.7', '458.8', '450.3', '440.7', '433.7', '432.0'], 'school_level': ['37.6', '34.4', '34.1', '35.5', '35.9', '37.7', '40.0', '32.3', '32.8', '34.2', '32.0', '31.4', '32.7', '32.8', '33.2', '34.9', '34.6', '33.1', '34.2', '31.3', '33.0', '29.2', '30.7', '32.7', '30.8', '33.7', '31.2', '32.6', '31.9', '32.3']}'484.3', '475.8', '467.9', '466.7', '458.8', '450.3', '440.7', '433.7', '432.0'], 'school_level': ['37.6', '34.4', '34.1', '35.5', '35.9', '37.7', '40.0', '32.3', '32.8', '34.2', '32.0', '31.4', '32.7', '32.8', '33.2', '34.9', '34.6', '33.1', '34.2', '31.3', '33.0', '29.2', '30.7', '32.7', '30.8', '33.7', '31.2', '32.6', '31.9', '32.3']}'484.3', '475.8', '467.9', '466.7', '458.8', '450.3', '440.7', '433.7', '432.0'], 'school_level': ['37.6', '34.4', '34.1', '35.5', '35.9', '37.7', '40.0', '32.3', '32.8', '34.2', '32.0', '31.4', '32.7', '32.8', '33.2', '34.9', '34.6', '33.1', '34.2', '31.3', '33.0', '29.2', '30.7', '32.7', '30.8', '33.7', '31.2', '32.6', '31.9', '32.3']}4', '34.1', '35.5', '35.9', '37.7', '40.0', '32.3', '32.8', '34.2', '32.0', '31.4', '32.7', '32.8', '33.2', '34.9', '34.6', '33.1', '34.2', '31.3', '33.0', '29.2', '30.7', '32.7', '30.8', '33.7', '31.2', '32.6', '31.9', '32.3']}4', '34.1', '35.5', '35.9', '37.7', '40.0', '32.3', '32.8', '34.2', '32.0', '31.4', '32.7', '32.8', '33.2', '34.9', '34.6', '33.1', '34.2', '31.3', '33.0', '29.2', '30.7', '32.7', '30.8', '33.7', '31.2', '32.6', '31.9', '32.3']}2', '32.6', '31.9', '32.3']}2', '32.6', '31.9', '32.3']}

import csv
date={ 'rank':['1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28','29','30'],'school_position':['北京','北京','浙江','上海','上海','江苏','安徽','湖北','湖北','陕西','四川','广东','黑龙江','上海','北京','江苏','北京','北京','北京','天津','天津','山东','湖南','陕西','广东','福建','吉林','上海','北京','四川'],'school_sum':['999.4','912.5','825.3','783.3','697.8','683.4','609.9','609.3','607.1','570.2','561.7','559.4','549.8','548.4','546.7','538.5','534.7','530.9','514.7','497.4','490.8','484.3','475.8','467.9','466.7','458.8','450.3','440.7','433.7','432.0'] } 

with open('F:/zly1.csv','w',encoding='utf8', newline='') as f: 
    w = csv.writer(f)
    fieldnames=['排名', '学校名称', '学校地点', '办学总分', '办学层级'] 
    # fieldnames=data.keys() 以字典原有键作为表头 
    w.writerow(fieldnames)
for x,y,z,m,n in zip(data['rank'],data['school_name'],data['school_sum'],data['school_position'],data['school_level']): 

    # w.writerow([x, y,z,m,n])
    for x in zip(*date.values()):
        print(x) 
        w.writerow([*x]) # 可改为 w.writerows([x])

The second method with open is the same as above

fieldnames=['排名','学校名称','学校地点','办学总分','办学层级']
# fieldnames=data.keys() #以字典原有键作为表头
if fieldnames is not None: 
    item = [str(i) for i in fieldnames] 
    f.write(','.join(item) + "\n") 
for item in zip(*data.values()): 
    item = [str(i) for i in item] 
    f.write(','.join(item) + "\n")

The third type: pandas library

a=pandas.DataFrame(data) 
lables=['排名','学校名称','学校地点','办学总分','办学层级']

a.to_csv('F:/zly.csv',header=lables,sep=',',index=False,encoding='utf8')

When used in conjunction with zip(...), writerow is a single line write, writing a list all to the same line of csv,

Writerows can write in multiple lines, writing each list of a two-dimensional list as a line

list nested dictionary export csv

# coding:utf-8 
date=[{'大学排名': '1', '大学名称': '清华大学 ', '省市': '北京', '类型': '综合', '总分': '852.5'}, {'大学排名': '2', '大学名称': '北京大学 ', '省市': '北京', '类型': '综合', '总分': '746.7'}, {'大学排名': '3', '大学名称': '浙江大学 ', '省市': '浙江', '类型': '综合', '总分': '649.2'}, {'大学排名': '4', '大学名称': '上海交通大学 ', '省市': '上海', '类型': '综合', '总分': '625.9'}, {'大学排名': '5', '大学名称': '南京大学 ', '省市': '江苏', '类型': '综合', '总分': '566.1'}, {'大学排名': '6', '大学名称': '复旦大学 ', '省市': '上海', '类型': '综合', '总分': '556.7'}, {'大学排名': '7', '大学名称': '中国科学技术大学 ', '省市': '安徽', '类型': '理工', '总分': '526.4'}, {'大学排名': '8', '大学名称': '华中科技大学 ', '省市': '湖北', '类型': '综合', '总分': '497.7'}, {'大学排名': '9', '大学名称': '武汉大学 ', '省市': '湖北', '类型': '综合', '总分': '488.0'}, {'大学排名': '10', '大学名称': '中山大学 ', '省市': '广东', '类型': '综合', '总分': '457.2'}, {'大学排名': '11', '大学名称': '西安交通大学 ', '省市': '陕西', '类型': '综合', '总分': '452.5'}, {'大学排名': '12', '大学名称': '哈尔滨工业大学 ', '省市': '黑龙江', '类型': '理工', '总分': '450.2'}, {'大学排名': '13', '大学名称': '北京航空航天大学 ', '省市': '北京', '类型': '理工', '总分': '445.1'}, {'大学排名': '14', '大学名称': '北京师范大学 ', '省市': '北京', '类型': '师范', '总分': '440.9'}, {'大学排名': '15', '大学名称': '同济大学 ', '省市': '上海', '类型': '理工', '总分': '439.0'}, {'大学排名': '16', '大学名称': '四川大学 ', '省市': '四川', '类型': '综合', '总分': '435.7'}, {'大学排名': '17', '大学名称': '东南大学 ', '省市': '江苏', '类型': '综合', '总分': '432.7'}, {'大学排名': '18', '大学名称': '中国人民大学 ', '省市': '北京', '类型': '综合', '总分': '409.7'}, {'大学排名': '19', '大学名称': '南开大学 ', '省市': '天津', '类型': '综合', '总分': '402.1'}, {'大学排名': '20', '大学名称': '北京理工大学 ', '省市': '北京', '类型': '理工', '总分': '395.6'}, {'大学排名': '21', '大学名称': '天津大学 ', '省市': '天津', '类型': '理工', '总分': '390.3'}, {'大学排名': '22', '大学名称': '山东大学 ', '省市': '山东', '类型': '综合', '总分': '387.9'}, {'大学排名': '23', '大学名称': '厦门大学 ', '省市': '福建', '类型': '综合', '总分': '383.3'}, {'大学排名': '24', '大学名称': '吉林大学 ', '省市': '吉林', '类型': '综合', '总分': '379.5'}, {'大学排名': '25', '大学名称': '华南理工大学 ', '省市': '广东', '类型': '理工', '总分': '379.4'}, {'大学排名': '26', '大学名称': '中南大学 ', '省市': '湖南', '类型': '综合', '总分': '378.6'}, {'大学排名': '27', '大学名称': '大连理工大学 ', '省市': '辽宁', '类型': '理工', '总分': '365.1'}, {'大学排名': '28', '大学名称': '西北工业大学 ', '省市': '陕西', '类型': '理工', '总分': '359.6'}]
with open('F:/zly1.csv','w',encoding='utf8', newline='') as f: 
    w = csv.writer(f) 
    # for x, y,z,m,n in zip(data['rank'],data['school_name'],data['school_sum'],data['school_position'],data['school_level']): 
    # w.writerow([x, y,z,m,n]) 
    w.writerow(date[0].keys()) 
    for x in date: 
        w.writerow(x.values())

 

^(*¯(oo)¯)^ Note:

If there are interval blank lines in the exported csv, you can add the newline parameter in with open

If there is a codec error, try adding # coding:utf-8 to the first line of the code