从python的角度来说:

res = pd.DataFrame(columns=('lib', 'qty1', 'qty2'))
res = res.append([{'qty1':10.0}], ignore_index=True)
print(res.head())

   lib  qty1  qty2
0  NaN  10.0   NaN

您可以使用生成器对象来创建一个Dataframe，这将在列表中更有效地使用内存。

num = 10

# Generator function to generate generator object
def numgen_func(num):
    for i in range(num):
        yield ('name_{}'.format(i), (i*i), (i*i*i))

# Generator expression to generate generator object (Only once data get populated, can not be re used)
numgen_expression = (('name_{}'.format(i), (i*i), (i*i*i)) for i in range(num) )

df = pd.DataFrame(data=numgen_func(num), columns=('lib', 'qty1', 'qty2'))

要向现有的数据帧添加raw，可以使用append方法。

df = df.append([{ 'lib': "name_20", 'qty1': 20, 'qty2': 400  }])

另一种方法(可能不是很有效):

# add a row
def add_row(df, row):
    colnames = list(df.columns)
    ncol = len(colnames)
    assert ncol == len(row), "Length of row must be the same as width of DataFrame: %s" % row
    return df.append(pd.DataFrame([row], columns=colnames))

你也可以像这样增强DataFrame类:

import pandas as pd
def add_row(self, row):
    self.loc[len(self.index)] = row
pd.DataFrame.add_row = add_row

pandas.DataFrame.append

DataFrame。append(self, other, ignore_index=False, verify_integrity=False, sort=False)→'数据帧'

Code

df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
df.append(df2)

ignore_index设置为True:

df.append(df2, ignore_index=True)

您还可以建立一个列表的列表，并将其转换为数据框架-

import pandas as pd

columns = ['i','double','square']
rows = []

for i in range(6):
    row = [i, i*2, i*i]
    rows.append(row)

df = pd.DataFrame(rows, columns=columns)

给

    i   double  square
0   0   0   0
1   1   2   1
2   2   4   4
3   3   6   9
4   4   8   16
5   5   10  25

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]