在Python中将树结构转换为csv的最有效方法的研究

Question

我有一个具有以下结构的树：

my_hash_pop = {
    "Europe" : {
        "France" : {
            "Paris" : 2220445,
            "Lille" : 225789,
            "Lyon" : 506615 },
        "Germany" : {
            "Berlin" : 3520031,
            "Munchen" : 1544041,
            "Dresden" : 540000 },
        },
    "South America" : {
        "Brasil" : {
            "Sao Paulo" : 11895893,
            "Rio de Janeiro" : 6093472 },
        "Argentina" : {
            "Salta" : 535303,
            "Buenos Aires" : 3090900 },
        },
    }

我想使用python将此结构转换为CSV：

Europe;Germany;Berlin;3520031
Europe;Germany;Munchen;1544041
Europe;Germany;Dresden;540000
Europe;France;Paris;2220445
Europe;France;Lyon;506615
Europe;France;Lille;225789
South America;Argentina;Buenos Aires;3090900
South America;Argentina;Salta;3090900
South America;Brasil;Sao Paulo;11895893
South America;Brasil;Rio de Janeiro;6093472

由于我的树在现实生活中包含大量的叶子（显然不是在这个例子中），我使用的转换脚本需要很长时间。 我试图找到一种更有效的方式来进行转换。 这是我尝试过的：

第一种方法：在每个叶子上连接字符串：

### METHOD 1 ###

start_1 = time.time()

data_to_write = ""

for region in my_hash_pop:
    for country in my_hash_pop[region]:
        for city in my_hash_pop[region][country]:
            data_to_write += region+";"+country+";"+city+";"+str(my_hash_pop[region][country][city])+"\n"

filename = "my_test_1.csv"
with open("my_test_1.csv", 'w+') as outfile:
    outfile.write(data_to_write)
    outfile.close()

end_1 = time.time()
print("---> METHOD 1 : Write all took " + str(end_1 - start_1) + "s")

第二种方法：用“检查点”连接字符串

### METHOD 2 ###

start_2 = time.time()

data_to_write = ""

for region in my_hash_pop:
    region_to_write = ""

    for country in my_hash_pop[region]:
        country_to_write = ""

        for city in my_hash_pop[region][country]:
            city_to_write = region+";"+country+";"+city+";"+str(my_hash_pop[region][country][city])+"\n"
            country_to_write += city_to_write

        region_to_write += country_to_write

    data_to_write += region_to_write

filename = "my_test_2.csv"
with open("my_test_2.csv", 'w+') as outfile:
    outfile.write(data_to_write)
    outfile.close()

end_2 = time.time()
print("---> METHOD 2 : Write all took " + str(end_2 - start_2) + "s")

第三种方法：使用Writer对象

### METHOD 3 ###

import csv

start_3 = time.time()

with open("my_test_3.csv", 'w+') as outfile:
    del_char = b";"
    w = csv.writer(outfile, delimiter=del_char)

    for region in my_hash_pop:
        for country in my_hash_pop[region]:
            for city in my_hash_pop[region][country]:
                w.writerow([region, country, city, str(my_hash_pop[region][country][city])])

end_3 = time.time()
print("---> METHOD 3 : Write all took " + str(end_3 - start_3) + "s")

比较每个方法在成长我的示例树时所花费的时间，我注意到方法1相当不合适。 在方法2和方法3之间，结果各不相同，并且不是那么明显（通常方法3似乎更有效）

因此，我有两个问题：

• 你看到我想尝试的另一种方法吗？
• 有没有更好的方法来检查和比较不同方法的效率？

还有一个奖励：

• 我注意到方法1和2的输出文件大小完全相同。 方法3的输出文件大于其他两个。 为什么？

谢谢你的贡献！

Answer 1

第三种方法是最有希望的。

您可以通过在每个级别使用items()来避免许多dict查找：

with open("my_test_3.csv", 'w+') as outfile:
    del_char = ";"
    w = csv.writer(outfile, delimiter=del_char)

    for region,countries in my_hash_pop.items():
        for country,cities in countries.items():
            for city,value in cities.items():
                w.writerow([region, country, city, value])

示例2和3之间的大小差异来自换行符 ： "\\n"表示'my_test_2.csv' ， "\\r\\n"表示'my_test_3.csv' 。 因此'my_test_3.csv'每一行都比'my_test_2.csv'大1个字节。

Answer 2

start_1 = time.time()
filename = "my_test_4.csv"
with open("my_test_4.csv", 'w+') as outfile:
    a = [outfile.write("%s;%s;%s;%s\n" % (k, kk, kkk, vvv))
         for (k, v) in my_hash_pop.items()
         for (kk, vv) in v.items()
         for (kkk, vvv) in vv.items()]
end_1 = time.time()
print("---> METHOD 1 : Write all took " + str(end_1 - start_1) + "s")

Answer 3

建议使用大熊猫，如下：

import pandas as pd
df = pd.DataFrame([(i,j,k,my_hash_pop[i][j][k])
                           for i in my_hash_pop.keys() 
                           for j in my_hash_pop[i].keys()
                           for k in my_hash_pop[i][j].keys()])

with open("my_test_4.csv", 'w') as outfile:
    outfile.write(df.to_csv(sep=';', header=False, index=False)))

我没有比较执行时间，也许使用熊猫不是你的选择，所以这只是一个建议。

Answer 4

在处理大型数据集时， panads非常有效。 下面是一个在pandas中导入json_normalize dict的方法，使用json_normalize展平然后你可以操作它。 例如写信给csv等

让我知道你的选择如何。

源代码

from pandas.io.json import json_normalize

df = json_normalize(my_hash_pop)

outfile = "temp.csv"
del_char = ";"

with open(outfile, 'wb+') as outfile:
    w = csv.writer(outfile, delimiter =';',quoting=csv.QUOTE_MINIMAL)
    for i in df.keys():
        s = ("{};{}").format(i.replace('.',';'),df[i][0]).split(";")
        w.writerow(s)