add one row in a pandas.DataFrame
I understand that pandas is designed to load fully populated DataFrame but I need to create an empty DataFrame then add rows, one by one . What is the best way to do this ?
I successfully created an empty DataFrame with :
res = DataFrame(columns=('lib', 'qty1', 'qty2'))
Then I can add a new row and fill a field with :
res = res.set_value(len(res), 'qty1', 10.0)
It works but seems very odd :-/ (it fails for adding string value)
How can I add a new row to my DataFrame (with different columns type) ?
@ Nasser的答案示例：
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(columns=['lib', 'qty1', 'qty2'])
>>> for i in range(5):
>>> df.loc[i] = [np.random.randint(-1,1) for n in range(3)]
>>>
>>> print(df)
lib qty1 qty2
0 0 0 -1
1 -1 -1 1
2 1 -1 1
3 0 0 0
4 1 -1 -1
[5 rows x 3 columns]
You could use pandas.concat() or DataFrame.append() . For details and examples, see Merge, join, and concatenate.
You could create a list of dictionaries, where each dictionary corresponds to an input data row. Once the list is complete, then create a data frame. This is a much faster approach.
I had a similar problem where if I created a data frame for each row and appended it to the main data frame it took 30 mins. On the other hand, if I used the below methodology, it was successful within seconds.
rows_list = []
for row in input_rows:
dict1 = {}
# get input row in dictionary format
# key = col_name
dict1.update(blah..)
rows_list.append(dict1)
df = pd.DataFrame(rows_list)