将列表或系列作为一行附加到熊猫DataFrame吗？

So I have initialized an empty pandas DataFrame and I would like to iteratively append lists (or Series) as rows in this DataFrame. What is the best way of doing this?

Sometimes it’s easier to do all the appending outside of pandas, then, just create the DataFrame in one shot.

>>> import pandas as pd
>>> simple_list=[['a','b']]
>>> simple_list.append(['e','f'])
>>> df=pd.DataFrame(simple_list,columns=['col1','col2'])
   col1 col2
0    a    b
1    e    f

df = pd.DataFrame(columns=list("ABC"))
df.loc[len(df)] = [1,2,3]

Here’s a simple and dumb solution:

>>> import pandas as pd
>>> df = pd.DataFrame()
>>> df = df.append({'foo':1, 'bar':2}, ignore_index=True)

Could you do something like this?

>>> import pandas as pd
>>> df = pd.DataFrame(columns=['col1', 'col2'])
>>> df = df.append(pd.Series(['a', 'b'], index=['col1','col2']), ignore_index=True)
>>> df = df.append(pd.Series(['d', 'e'], index=['col1','col2']), ignore_index=True) 
>>> df
  col1 col2
0    a    b
1    d    e

Does anyone have a more elegant solution?

Following onto Mike Chirico’s answer… if you want to append a list after the dataframe is already populated…

>>> list = [['f','g']]
>>> df = df.append(pd.DataFrame(list, columns=['col1','col2']),ignore_index=True)
>>> df
  col1 col2
0    a    b
1    d    e
2    f    g

If you want to add a Series and use the Series’ index as columns of the DataFrame, you only need to append the Series between brackets:

In [1]: import pandas as pd

In [2]: df = pd.DataFrame()

In [3]: row=pd.Series([1,2,3],["A","B","C"])

In [4]: row
Out[4]: 
A    1
B    2
C    3
dtype: int64

In [5]: df.append([row],ignore_index=True)
Out[5]: 
   A  B  C
0  1  2  3

[1 rows x 3 columns]

Whitout the ignore_index=True you don’t get proper index.

Here’s a function that, given an already created dataframe, will append a list as a new row. This should probably have error catchers thrown in, but if you know exactly what you’re adding then it shouldn’t be an issue.

import pandas as pd
import numpy as np

def addRow(df,ls):
    """
    Given a dataframe and a list, append the list as a new row to the dataframe.

    :param df: <DataFrame> The original dataframe
    :param ls: <list> The new row to be added
    :return: <DataFrame> The dataframe with the newly appended row
    """

    numEl = len(ls)

    newRow = pd.DataFrame(np.array(ls).reshape(1,numEl), columns = list(df.columns))

    df = df.append(newRow, ignore_index=True)

    return df

Converting the list to a data frame within the append function works, also when applied in a loop

import pandas as pd
mylist = [1,2,3]
df = pd.DataFrame()
df = df.append(pd.DataFrame(data[mylist]))

simply use loc:

>>> df
     A  B  C
one  1  2  3
>>> df.loc["two"] = [4,5,6]
>>> df
     A  B  C
one  1  2  3
two  4  5  6

As mentioned here – https://kite.com/python/answers/how-to-append-a-list-as-a-row-to-a-pandas-dataframe-in-python, you’ll need to first convert the list to a series then append the series to dataframe.

df = pd.DataFrame([[1, 2], [3, 4]], columns = ["a", "b"])
to_append = [5, 6]
a_series = pd.Series(to_append, index = df.columns)
df = df.append(a_series, ignore_index=True)

The simplest way:

my_list = [1,2,3,4,5]
df['new_column'] = pd.Series(my_list).values

Edit:

Don’t forget that the length of the new list should be the same of the corresponding Dataframe.