如果你有一个数据帧df，想要添加一个列表new_list作为一个新行到df，你可以简单地做:

df.loc[len(df)] = new_list

如果你想在数据帧df下添加一个新的数据帧new_df，那么你可以使用:

df.append(new_df)

在向dataframe添加大量行的情况下，我对性能感兴趣。所以我尝试了四种最流行的方法，并检查了它们的速度。

性能

使用.append (NPE的答案) 使用。loc (fred的回答) 使用.loc预分配(FooBar的答案) 使用dict并最终创建DataFrame (ShikharDua的回答)

运行时结果(秒):

所以我自己用了加法法。

代码:

import pandas as pd
import numpy as np
import time

del df1, df2, df3, df4
numOfRows = 1000
# append
startTime = time.perf_counter()
df1 = pd.DataFrame(np.random.randint(100, size=(5,5)), columns=['A', 'B', 'C', 'D', 'E'])
for i in range( 1,numOfRows-4):
    df1 = df1.append( dict( (a,np.random.randint(100)) for a in ['A','B','C','D','E']), ignore_index=True)
print('Elapsed time: {:6.3f} seconds for {:d} rows'.format(time.perf_counter() - startTime, numOfRows))
print(df1.shape)

# .loc w/o prealloc
startTime = time.perf_counter()
df2 = pd.DataFrame(np.random.randint(100, size=(5,5)), columns=['A', 'B', 'C', 'D', 'E'])
for i in range( 1,numOfRows):
    df2.loc[i]  = np.random.randint(100, size=(1,5))[0]
print('Elapsed time: {:6.3f} seconds for {:d} rows'.format(time.perf_counter() - startTime, numOfRows))
print(df2.shape)

# .loc with prealloc
df3 = pd.DataFrame(index=np.arange(0, numOfRows), columns=['A', 'B', 'C', 'D', 'E'] )
startTime = time.perf_counter()
for i in range( 1,numOfRows):
    df3.loc[i]  = np.random.randint(100, size=(1,5))[0]
print('Elapsed time: {:6.3f} seconds for {:d} rows'.format(time.perf_counter() - startTime, numOfRows))
print(df3.shape)

# dict
startTime = time.perf_counter()
row_list = []
for i in range (0,5):
    row_list.append(dict( (a,np.random.randint(100)) for a in ['A','B','C','D','E']))
for i in range( 1,numOfRows-4):
    dict1 = dict( (a,np.random.randint(100)) for a in ['A','B','C','D','E'])
    row_list.append(dict1)

df4 = pd.DataFrame(row_list, columns=['A','B','C','D','E'])
print('Elapsed time: {:6.3f} seconds for {:d} rows'.format(time.perf_counter() - startTime, numOfRows))
print(df4.shape)

附注:我相信我的实现并不完美，也许还有一些优化可以做。

mycolumns = ['A', 'B']
df = pd.DataFrame(columns=mycolumns)
rows = [[1,2],[3,4],[5,6]]
for row in rows:
    df.loc[len(df)] = row

简单点。通过将一个列表作为输入，该列表将作为一行添加到数据帧中:

import pandas as pd
res = pd.DataFrame(columns=('lib', 'qty1', 'qty2'))
for i in range(5):
    res_list = list(map(int, input().split()))
    res = res.append(pd.Series(res_list, index=['lib', 'qty1', 'qty2']), ignore_index=True)

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] 

可以使用ignore_index选项将单行追加为字典。

>>> f = pandas.DataFrame(data = {'Animal':['cow','horse'], 'Color':['blue', 'red']})
>>> f
  Animal Color
0    cow  blue
1  horse   red
>>> f.append({'Animal':'mouse', 'Color':'black'}, ignore_index=True)
  Animal  Color
0    cow   blue
1  horse    red
2  mouse  black