如何合并两个数据帧？

I’m using Pandas data frames. I have a initial data frame, say D. I extract two data frames from it like this:

A = D[D.label == k]
B = D[D.label != k]

I want to combine A and B so I can have them as one DataFrame, something like a union operation. The order of the data is not important. However, when we sample A and B from D, they retain their indexes from D.

I believe you can use the append method

bigdata = data1.append(data2, ignore_index=True)

to keep their indexes just dont use the ignore_index keyword …

You can also use pd.concat, which is particularly helpful when you are joining more than two dataframes:

bigdata = pd.concat([data1, data2], ignore_index=True, sort=False)

Thought to add this here in case someone finds it useful. @ostrokach already mentioned how you can merge the data frames across rows which is

df_row_merged = pd.concat([df_a, df_b], ignore_index=True)

To merge across columns, you can use the following syntax:

df_col_merged = pd.concat([df_a, df_b], axis=1)

There’s another solution for the case that you are working with big data and need to concatenate multiple datasets. concat can get performance-intensive, so if you don’t want to create a new df each time, you can instead use a list comprehension:

frames = [ process_file(f) for f in dataset_files ]
result = pd.append(frames)

(as pointed out here in the docs at the bottom of the section):

Note: It is worth noting however, that concat (and therefore append) makes a full copy of the data, and that constantly reusing this function can create a significant performance hit. If you need to use the operation over several datasets, use a list comprehension.

If you want to update/replace the values of first dataframe df1 with the values of second dataframe df2. you can do it by following steps —

Step 1: Set index of the first dataframe (df1)

df1.set_index('id')

Step 2: Set index of the second dataframe (df2)

df2.set_index('id')

and finally update the dataframe using the following snippet —

df1.update(df2)

1st dataFrame

train.shape

result:-

(31962, 3)

2nd dataFrame

test.shape

result:-

(17197, 2)

Combine

new_data=train.append(test,ignore_index=True)

Check

new_data.shape

result:-

(49159, 3)