Appending a list or series to a pandas DataFrame as a row?

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?

Here’s a simple and dumb solution:

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

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.

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

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

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

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.

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]))

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)

There are several ways to append a list to a Pandas Dataframe in Python. Let’s consider the following dataframe and list:

import pandas as pd
# Dataframe
df = pd.DataFrame([[1, 2], [3, 4]], columns = ["col1", "col2"])
# List to append
list = [5, 6]

Option 1: append the list at the end of the dataframe with pandas.DataFrame.loc.

df.loc[len(df)] = list

Option 2: convert the list to dataframe and append with pandas.DataFrame.append().

df = df.append(pd.DataFrame([list], columns=df.columns), ignore_index=True)

Option 3: convert the list to series and append with pandas.DataFrame.append().

df = df.append(pd.Series(list, index = df.columns), ignore_index=True)

Each of the above options should output something like:

>>> print (df)
   col1  col2
0     1     2
1     3     4
2     5     6

Reference : How to append a list as a row to a Pandas DataFrame in Python?

Consider an array A of N x 2 dimensions. To add one more row, use the following.

A.loc[A.shape[0]] = [3,4]