向pandas DataFrame添加一行_dataframe添加一行数据

本文翻译自：Add one row to pandas DataFrame

I understand that pandas is designed to load fully populated DataFrame but I need to create an empty DataFrame then add rows, one by one . 我知道pandas旨在加载完全填充的DataFrame但是我需要创建一个空的DataFrame，然后逐行添加行 。 What is the best way to do this ? 做这个的最好方式是什么 ？

I successfully created an empty DataFrame with : 我成功创建了一个空的DataFrame：

res = DataFrame(columns=('lib', 'qty1', 'qty2'))

Then I can add a new row and fill a field with : 然后，我可以添加新行，并使用以下字段填充字段：

res = res.set_value(len(res), 'qty1', 10.0)

It works but seems very odd :-/ (it fails for adding string value) 它有效，但看起来很奇怪：-/（添加字符串值失败）

How can I add a new row to my DataFrame (with different columns type) ? 如何将新行添加到DataFrame（具有不同的列类型）？

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#1楼

参考：https://stackoom.com/question/ixi9/向pandas-DataFrame添加一行

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#2楼

You could use pandas.concat() or DataFrame.append() . 您可以使用pandas.concat()或DataFrame.append() 。 For details and examples, see Merge, join, and concatenate . 有关详细信息和示例，请参见合并，联接和连接 。

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#3楼

In case you can get all data for the data frame upfront, there is a much faster approach than appending to a data frame: 如果可以预先获取该数据帧的所有数据，则有一种比附加到数据帧快得多的方法：

1. Create a list of dictionaries in which each dictionary corresponds to an input data row. 创建一个词典列表，其中每个词典对应于一个输入数据行。
2. Create a data frame from this list. 从此列表创建一个数据框。

I had a similar task for which appending to a data frame row by row took 30 min, and creating a data frame from a list of dictionaries completed within seconds. 我有一个类似的任务，需要花30分钟的时间逐行追加到数据帧，然后在几秒钟内完成的字典列表中创建数据帧。

rows_list = []
for row in input_rows:
 
        dict1 = {}
        # get input row in dictionary format
        # key = col_name
        dict1.update(blah..) 
 
        rows_list.append(dict1)
 
df = pd.DataFrame(rows_list)               

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#4楼

For efficient appending see How to add an extra row to a pandas dataframe and Setting With Enlargement . 为了高效地附加，请参见如何向pandas数据框添加额外的行和“ 设置为放大” 。

Add rows through loc/ix on non existing key index data. 通过loc/ix在不存在的键索引数据上添加行。 eg : 例如：

In [1]: se = pd.Series([1,2,3])
 
In [2]: se
Out[2]: 
0    1
1    2
2    3
dtype: int64
 
In [3]: se[5] = 5.
 
In [4]: se
Out[4]: 
0    1.0
1    2.0
2    3.0
5    5.0
dtype: float64

Or: 要么：

In [1]: dfi = pd.DataFrame(np.arange(6).reshape(3,2),
   .....:                 columns=['A','B'])
   .....: 
 
In [2]: dfi
Out[2]: 
   A  B
0  0  1
1  2  3
2  4  5
 
In [3]: dfi.loc[:,'C'] = dfi.loc[:,'A']
 
In [4]: dfi
Out[4]: 
   A  B  C
0  0  1  0
1  2  3  2
2  4  5  4
In [5]: dfi.loc[3] = 5
 
In [6]: dfi
Out[6]: 
   A  B  C
0  0  1  0
1  2  3  2
2  4  5  4
3  5  5  5

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#5楼

>>> import pandas as pd
>>> from numpy.random import randint
>>> df = pd.DataFrame(columns=['lib', 'qty1', 'qty2'])
>>> for i in range(5):
>>>     df.loc[i] = ['name' + str(i)] + list(randint(10, size=2))
>>> df
     lib qty1 qty2
0  name0    3    3
1  name1    2    4
2  name2    2    8
3  name3    2    1
4  name4    9    6

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#6楼

If you know the number of entries ex ante, you should preallocate the space by also providing the index (taking the data example from a different answer): 如果您事前知道条目数，则应该通过提供索引来预分配空间（从另一个答案中获取数据示例）：

import pandas as pd
import numpy as np
# we know we're gonna have 5 rows of data
numberOfRows = 5
# create dataframe
df = pd.DataFrame(index=np.arange(0, numberOfRows), columns=('lib', 'qty1', 'qty2') )
 
# now fill it up row by row
for x in np.arange(0, numberOfRows):
    #loc or iloc both work here since the index is natural numbers
    df.loc[x] = [np.random.randint(-1,1) for n in range(3)]
In[23]: df
Out[23]: 
   lib  qty1  qty2
0   -1    -1    -1
1    0     0     0
2   -1     0    -1
3    0    -1     0
4   -1     0     0

Speed comparison 速度比较

In[30]: %timeit tryThis() # function wrapper for this answer
In[31]: %timeit tryOther() # function wrapper without index (see, for example, @fred)
1000 loops, best of 3: 1.23 ms per loop
100 loops, best of 3: 2.31 ms per loop

And - as from the comments - with a size of 6000, the speed difference becomes even larger: 而且-从注释中看-大小为6000，速度差异变得更大：

Increasing the size of the array (12) and the number of rows (500) makes the speed difference more striking: 313ms vs 2.29s 增加数组（12）的大小和行数（500）会使速度差异更加明显：313ms vs 2.29s