是否可以使用熊猫在数据框中的任意位置插入一行？

我有一个类似于下面这个的 DataFrame 对象:

       onset    length
1      2.215    1.3
2     23.107    1.3
3     41.815    1.3
4     61.606    1.3
...

我想做的是插入一个行的位置指定的一些索引值，并更新以下索引相应。例如:

       onset    length
1      2.215    1.3
2     23.107    1.3
3     30.000    1.3  # new row
4     41.815    1.3
5     61.606    1.3
...

最好的方法是什么？

93853 次浏览

小开

最佳答案

You could slice and use concat to get what you want.

line = DataFrame({"onset": 30.0, "length": 1.3}, index=[3])
df2 = concat([df.iloc[:2], line, df.iloc[2:]]).reset_index(drop=True)

This will produce the dataframe in your example output. As far as I'm aware, concat is the best method to achieve an insert type operation in pandas, but admittedly I'm by no means a pandas expert.

小开

I find it more readable to sort rather than slice and concatenate.

line = DataFrame({"onset": 30.0, "length": 1.3}, index=[2.5])
df = df.append(line, ignore_index=False)
df = df.sort_index().reset_index(drop=True)

小开

I think it's even easier without concat or append:

df.loc[2.5] = 30.0, 1.3
df = df.sort_index().reset_index(drop=True)

(Supposing that the index is as provided, starting from 1)

小开

If you want to keep the original indexes this might work beter:

df = pd.DataFrame(dict(x=[0, 1, 2, 3, 4]))
df_update = pd.DataFrame(dict(x=[10, 11, 12]), index=[3, 4, 5])


# concat df_update first
df = pd.concat([df_update, df], axis=0)


# drop duplicates, updates will be prioritized
df = df.iloc[df.index.drop_duplicates()]


# sort to regain order
df.sort_index(inplace=True)