将行追加到DataFrame的最快，最有效的方法是什么? [英] What is the fastest and most efficient way to append rows to a DataFrame?

问题描述

我有一个很大的数据集，必须将其转换为.csv格式，我有29列和超过一百万行.我正在使用python和pandas数据框来处理此工作.我认为，随着数据框变大，将任何行追加到它会越来越耗时.我想知道是否有更快的方法，可以共享代码中的相关代码段.

I have a large dataset which I have to convert to .csv format, I have 29 columns and more than a million lines. I am using python and pandas dataframe to handle this job. I figured that as the dataframe gets larger, appending any rows to is it getting more and more time consuming. I wonder if there is any faster way to this, sharing the relevant snippet from the code.

欢迎任何建议.

df = DataFrame()

for startID in range(0, 100000, 1000):
    s1 = time.time()
    tempdf = DataFrame()
    url = f'https://******/products?startId={startID}&size=1000'

    r = requests.get(url, headers={'****-Token': 'xxxxxx', 'Merchant-Id': '****'})
    jsonList = r.json()  # datatype= list, contains= dict

    normalized = json_normalize(jsonList)
    # type(normal) = pandas.DataFrame
    print(startID / 1000) # status indicator
    for series in normalized.iterrows():  
        series = series[1] # iterrows returns tuple (index, series)
        offers = series['offers']
        series = series.drop(columns='offers')
        length = len(offers)

        for offer in offers:
            n = json_normalize(offer).squeeze()  # squeeze() casts DataFrame into Series
            concatinated = concat([series, n]).to_frame().transpose()
            tempdf = tempdf.append(concatinated, ignore_index=True)

    del normalized
    df = df.append(tempdf)
    f1 = time.time()
    print(f1 - s1, ' seconds')

df.to_csv('out.csv')

推荐答案

正如Mohit Motwani建议的最快方法是将数据收集到字典中，然后将所有内容加载到数据帧中.下面是一些速度测量示例:

As Mohit Motwani suggested fastest way is to collect data into dictionary then load all into data frame. Below some speed measurements examples:

import pandas as pd
import numpy as np
import time
import random

end_value = 10000

用于创建字典的度量，最后将其全部加载到数据帧中

Measurement for creating dictionary and at the end load all into data frame

start_time = time.time()
dictinary_list = []
for i in range(0, end_value, 1):
    dictionary_data = {k: random.random() for k in range(30)}
    dictinary_list.append(dictionary_data)

df_final = pd.DataFrame.from_dict(dictinary_list)

end_time = time.time()
print('Execution time = %.6f seconds' % (end_time-start_time))

执行时间= 0.090153秒

Execution time = 0.090153 seconds

将数据附加到列表中并将concat附加到数据框中的度量:

Measurements for appending data into list and concat into data frame:

start_time = time.time()
appended_data = []
for i in range(0, end_value, 1):
    data = pd.DataFrame(np.random.randint(0, 100, size=(1, 30)), columns=list('A'*30))
    appended_data.append(data)

appended_data = pd.concat(appended_data, axis=0)

end_time = time.time()
print('Execution time = %.6f seconds' % (end_time-start_time))

执行时间= 4.183921秒

Execution time = 4.183921 seconds

用于附加数据帧的度量:

Measurements for appending data frames:

start_time = time.time()
df_final = pd.DataFrame()
for i in range(0, end_value, 1):
    df = pd.DataFrame(np.random.randint(0, 100, size=(1, 30)), columns=list('A'*30))
    df_final = df_final.append(df)

end_time = time.time()
print('Execution time = %.6f seconds' % (end_time-start_time))

执行时间= 11.085888秒

Execution time = 11.085888 seconds

使用loc进行插入数据的测量:

Measurements for insert data by usage of loc:

start_time = time.time()
df = pd.DataFrame(columns=list('A'*30))
for i in range(0, end_value, 1):
    df.loc[i] = list(np.random.randint(0, 100, size=30))


end_time = time.time()
print('Execution time = %.6f seconds' % (end_time-start_time))

执行时间= 21.029176秒

Execution time = 21.029176 seconds

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