[英]Fastest way to add rows to existing pandas dataframe
我目前正在嘗試根據現有的csv創建一個新的csv。
我找不到基於現有數據框值設置數據框值的更快方法。
import pandas
import sys
import numpy
import time
# path to file as argument
path = sys.argv[1]
df = pandas.read_csv(path, sep = "\t")
# only care about lines with response_time
df = df[pandas.notnull(df['response_time'])]
# new empty dataframe
new_df = pandas.DataFrame(index = df["datetime"])
# new_df needs to have datetime as index
# and columns based on a combination
# of 2 columns name from previous dataframe
# (there are only 10 differents combinations)
# and response_time as values, so there will be lots of
# blank cells but I don't care
for i, row in df.iterrows():
start = time.time()
new_df.set_value(row["datetime"], row["name"] + "-" + row["type"], row["response_time"])
print(i, time.time() - start)
原始數據框為:
datetime name type response_time
0 2018-12-18T00:00:00.500829 HSS_ANDROID audio 0.02430
1 2018-12-18T00:00:00.509108 HSS_ANDROID video 0.02537
2 2018-12-18T00:00:01.816758 HSS_TEST audio 0.03958
3 2018-12-18T00:00:01.819865 HSS_TEST video 0.03596
4 2018-12-18T00:00:01.825054 HSS_ANDROID_2 audio 0.02590
5 2018-12-18T00:00:01.842974 HSS_ANDROID_2 video 0.03643
6 2018-12-18T00:00:02.492477 HSS_ANDROID audio 0.01575
7 2018-12-18T00:00:02.509231 HSS_ANDROID video 0.02870
8 2018-12-18T00:00:03.788196 HSS_TEST audio 0.01666
9 2018-12-18T00:00:03.807682 HSS_TEST video 0.02975
new_df將如下所示:
我每個循環需要7毫秒。
處理(僅?個)400 000行數據框需要很長時間。 我怎樣才能使其更快?
確實,使用pivot
將滿足您的需求,例如:
import pandas as pd
new_df = pd.pivot(df.datetime, df.name + '-' + df.type, df.response_time)
print (new_df.head())
HSS_ANDROID-audio HSS_ANDROID-video \
datetime
2018-12-18T00:00:00.500829 0.0243 NaN
2018-12-18T00:00:00.509108 NaN 0.02537
2018-12-18T00:00:01.816758 NaN NaN
2018-12-18T00:00:01.819865 NaN NaN
2018-12-18T00:00:01.825054 NaN NaN
HSS_ANDROID_2-audio HSS_ANDROID_2-video \
datetime
2018-12-18T00:00:00.500829 NaN NaN
2018-12-18T00:00:00.509108 NaN NaN
2018-12-18T00:00:01.816758 NaN NaN
2018-12-18T00:00:01.819865 NaN NaN
2018-12-18T00:00:01.825054 0.0259 NaN
HSS_TEST-audio HSS_TEST-video
datetime
2018-12-18T00:00:00.500829 NaN NaN
2018-12-18T00:00:00.509108 NaN NaN
2018-12-18T00:00:01.816758 0.03958 NaN
2018-12-18T00:00:01.819865 NaN 0.03596
2018-12-18T00:00:01.825054 NaN NaN
並且沒有NaN
,您可以將fillna
與所需的任何值一起使用,例如:
new_df = pd.pivot(df.datetime, df.name +'-'+df.type, df.response_time).fillna(0)
您也可以使用unstack
作為另一種選擇
new = df.set_index(['type','name', 'datetime']).unstack([0,1])
new.columns = ['{}-{}'.format(z,y) for x,y,z, in new.columns]
使用f-strings
會比format
快一點:
new.columns = [f'{z}-{y}' for x,y,z, in new.columns]
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