如果你想在末尾添加一行，将其作为列表追加:

valuestoappend = [va1, val2, val3]
res = res.append(pd.Series(valuestoappend, index = ['lib', 'qty1', 'qty2']), ignore_index = True)

如果你的Dataframe中的所有数据都有相同的dtype，你可以使用NumPy数组。您可以直接将行写入预定义数组，并在最后将其转换为数据框架。 它似乎比转换字典列表还要快。

import pandas as pd
import numpy as np
from string import ascii_uppercase

startTime = time.perf_counter()
numcols, numrows = 5, 10000
npdf = np.ones((numrows, numcols))
for row in range(numrows):
    npdf[row, 0:] = np.random.randint(0, 100, (1, numcols))
df5 = pd.DataFrame(npdf, columns=list(ascii_uppercase[:numcols]))
print('Elapsed time: {:6.3f} seconds for {:d} rows'.format(time.perf_counter() - startTime, numOfRows))
print(df5.shape)

initial_data = {'lib': np.array([1,2,3,4]), 'qty1': [1,2,3,4], 'qty2': [1,2,3,4]}

df = pd.DataFrame(initial_data)

df

lib    qty1    qty2
0    1    1    1
1    2    2    2
2    3    3    3
3    4    4    4

val_1 = [10]
val_2 = [14]
val_3 = [20]

df.append(pd.DataFrame({'lib': val_1, 'qty1': val_2, 'qty2': val_3}))

lib    qty1    qty2
0    1    1    1
1    2    2    2
2    3    3    3
3    4    4    4
0    10    14    20

可以使用for循环遍历值，也可以添加值数组。

val_1 = [10, 11, 12, 13]
val_2 = [14, 15, 16, 17]
val_3 = [20, 21, 22, 43]

df.append(pd.DataFrame({'lib': val_1, 'qty1': val_2, 'qty2': val_3}))

lib    qty1    qty2
0    1    1    1
1    2    2    2
2    3    3    3
3    4    4    4
0    10    14    20
1    11    15    21
2    12    16    22
3    13    17    43

如果你事先知道条目的数量，你应该通过提供索引来预分配空间(从不同的答案中获得数据示例):

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

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

而且，从评论中可以看出，如果尺寸为6000，速度差异会变得更大:

增加数组的大小(12)和行数(500)使 速度上的差异更加显著:313毫秒vs 2.29秒

有关有效附加，请参见如何向pandas数据框架添加额外行和使用放大设置。

通过loc/ix在不存在的键索引数据上添加行。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:

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

你只需要loc[df]。形状[0]]或loc[len(df)]

# Assuming your df has 4 columns (str, int, str, bool)
df.loc[df.shape[0]] = ['col1Value', 100, 'col3Value', False] 

or

df.loc[len(df)] = ['col1Value', 100, 'col3Value', False]