How do I combine two dataframes?
Question:
I have a initial dataframe 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
into one DataFrame. The order of the data is not important. However, when we sample A
and B
from D
, they retain their indexes from D
.
Answers:
DEPRECATED: DataFrame.append
and Series.append
were deprecated in v1.4.0.
Use append
:
df_merged = df1.append(df2, ignore_index=True)
And to keep their indexes, set ignore_index=False
.
Use pd.concat
to join multiple dataframes:
df_merged = pd.concat([df1, df2], ignore_index=True, sort=False)
Merge across rows:
df_row_merged = pd.concat([df_a, df_b], ignore_index=True)
Merge across columns:
df_col_merged = pd.concat([df_a, df_b], axis=1)
If you’re working with big data and need to concatenate multiple datasets calling concat
many times can get performance-intensive.
If you don’t want to create a new df each time, you can instead aggregate the changes and call concat
only once:
frames = [df_A, df_B] # Or perform operations on the DFs
result = pd.concat(frames)
This is pointed out in the pandas docs under concatenating objects 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)
# collect excel content into list of dataframes
data = []
for excel_file in excel_files:
data.append(pd.read_excel(excel_file, engine="openpyxl"))
# concatenate dataframes horizontally
df = pd.concat(data, axis=1)
# save combined data to excel
df.to_excel(excelAutoNamed, index=False)
You can try the above when you are appending horizontally! Hope this helps sum1
Use this code to attach two Pandas Data Frames horizontally:
df3 = pd.concat([df1, df2],axis=1, ignore_index=True, sort=False)
You must specify around what axis you intend to merge two frames.
To join 2 pandas dataframes by column, using their indices as the join key, you can do this:
both = a.join(b)
And if you want to join multiple DataFrames, Series, or a mixture of them, by their index, just put them in a list, e.g.,:
everything = a.join([b, c, d])
See the pandas docs for DataFrame.join()
.
Both the dataframe should have same column name else instead of appending records by row wise, it will append as separate columns.
df = df.append(df1,ignore_index=True)
df = pd.concat([df1,df2], ignore_index=True)
I have a initial dataframe 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
into one DataFrame. The order of the data is not important. However, when we sample A
and B
from D
, they retain their indexes from D
.
DEPRECATED:
DataFrame.append
andSeries.append
were deprecated in v1.4.0.
Use append
:
df_merged = df1.append(df2, ignore_index=True)
And to keep their indexes, set ignore_index=False
.
Use pd.concat
to join multiple dataframes:
df_merged = pd.concat([df1, df2], ignore_index=True, sort=False)
Merge across rows:
df_row_merged = pd.concat([df_a, df_b], ignore_index=True)
Merge across columns:
df_col_merged = pd.concat([df_a, df_b], axis=1)
If you’re working with big data and need to concatenate multiple datasets calling concat
many times can get performance-intensive.
If you don’t want to create a new df each time, you can instead aggregate the changes and call concat
only once:
frames = [df_A, df_B] # Or perform operations on the DFs
result = pd.concat(frames)
This is pointed out in the pandas docs under concatenating objects at the bottom of the section):
Note: It is worth noting however, that
concat
(and thereforeappend
)
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)
# collect excel content into list of dataframes
data = []
for excel_file in excel_files:
data.append(pd.read_excel(excel_file, engine="openpyxl"))
# concatenate dataframes horizontally
df = pd.concat(data, axis=1)
# save combined data to excel
df.to_excel(excelAutoNamed, index=False)
You can try the above when you are appending horizontally! Hope this helps sum1
Use this code to attach two Pandas Data Frames horizontally:
df3 = pd.concat([df1, df2],axis=1, ignore_index=True, sort=False)
You must specify around what axis you intend to merge two frames.
To join 2 pandas dataframes by column, using their indices as the join key, you can do this:
both = a.join(b)
And if you want to join multiple DataFrames, Series, or a mixture of them, by their index, just put them in a list, e.g.,:
everything = a.join([b, c, d])
See the pandas docs for DataFrame.join()
.
Both the dataframe should have same column name else instead of appending records by row wise, it will append as separate columns.
df = df.append(df1,ignore_index=True)
df = pd.concat([df1,df2], ignore_index=True)