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

Question

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?

Answer 1

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.

Answer 2

Here's a simple and dumb solution:

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

Answer 3

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.

Answer 4

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

Answer 5

Overview: you can use loc to append a list to an existing dataframe

  win_results_df=pd.DataFrame(columns=['GameId',
  'Team',
  'TeamOpponent',
  'HomeScore',
  'VisitorScore','Target'])

for key,item in teams.items():
    game_filter=(games_df['homeDisplayName']==key)
    win_df=games_df[game_filter]['HomeScore']>games_df[game_filter]['VisitorScore']
    for oppKey,teamOpponent in games_df[game_filter][win_df].iterrows():
       df_length = len(win_results_df)
       win_results_df.loc[df_length] = [teamOpponent['gameId'],key,teamOpponent['visitorDisplayName'],teamOpponent['HomeScore'],teamOpponent['VisitorScore'],True]

Answer 6

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