python数据框追加_Python - 高效的方式向数据框添加行

From this question and others it seems that it is not recommended to use concat or append to build a pandas dataframe because it is recopying the whole dataframe each time.

My project involves retrieving a small amount of data every 30 seconds. This might run for a 3 day weekend, so someone could easily expect over 8000 rows to be created one row at a time. What would be the most efficient way to add rows to this dataframe?

解决方案

You can add rows to a DataFrame in-place using loc on a non-existent index. From the Pandas documentation:

In [119]: dfi

Out[119]:

A B C

0 0 1 0

1 2 3 2

2 4 5 4

In [120]: dfi.loc[3] = 5

In [121]: dfi

Out[121]:

A B C

0 0 1 0

1 2 3 2

2 4 5 4

3 5 5 5

As expected, using loc is considerably faster than append (about 14x):

import pandas as pd

df = pd.DataFrame({"A": [1,2,3], "B": [1,2,3], "C": [1,2,3]})

%%timeit

df2 = pd.DataFrame({"A": [4], "B": [4], "C": [4]})

df.append(df2)

# 1000 loops, best of 3: 1.61 ms per loop

%%timeit

df.loc[3] = 4

# 10000 loops, best of 3: 113 µs per loop