How do I join together 4 DataFrames? The names of the DataFrames are called df1, df2, df3, and df4.
They are all the same column size and I am trying to use the 'inner' join.
How would I modify this code to make it work for all four?
I tried using this code and it worked to combine two of them, but I could not figure out how to write it to work for all four DataFrames.
dfJoin = df1.join(df2,how='inner')
print(dfJoin)
You just have to chain together the joins.
dfJoin = df1.join(df2, how="inner", on="common_column") /
.join(df3, how="inner", on="common_column") /
.join(df4, how="inner", on="common_column")
or if you have more than 4, just put them in a list df_list and iterate through it.
Joining or appending multiple DataFrames in one go can be down with pd.concat():
list_of_df = [df1, df2, df3, df4]
df = pd.concat(list_of_df, how=“inner”)
Your question does now state if you want them merged column- or index-wise, but since your state that they have the same number of columns, I assume you wish to append said DataFrames. For this case the code above works. If you want to make a wide DataFrame, change the attribute axisto 1.
Related
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 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)
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().
# 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.
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 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)
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().
# 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.
I have a list of DataFrames [df1, df2, df3, df4, ...]
I want to do pd.concat, and only use the columns of a df it doesn't exist in previous dfs
dfs = [df1, df2, df3, df4, ...]
final_df = df1
for df in dfs[1:]:
final_df = pd.concat([final_df, df[df.columns.difference(final_df.columns)]])
Is there a better way than what I did above?
You can achieve this using sets. consider the following example:
s1 = set(('col1', 'col2'))
s2 = set(('col2', 'col3'))
s2.difference(s1) # this returns {'col3'}
So what you can do is accumulate a set of column names and for every new column append it to your data frame and the column name to the accumulated set.
On further reading of you question, it appears that you did pretty much the same thing. the only improvement that I can think of is if you have many frame and/or large frames then don't concat on every iteration because that creates a new frame and that will get slower and slower. Instead put all of them in a list and make a frame of them in the end.
Welcome, I have a simple question, to which I haven't found a solution.
I have two dataframes df1 and df2:
df1 contains several columns and a multiindex as year-month-week
df2 contains the multiindex year-week with only one column in the df.
I would like to create an inner join of df1 and df2, joining on 'year' and 'week'.
I have tried to do the following:
df1['newcol'] = df1.index.get_level_values(2).map(lambda x: df2.newcol[x])
Which only joins on month (or year?), is there any way to expand it so that the merge is actually right?
Thanks in advance!
df1
df2
Eventually i solved with with removing the multiindex and doing a good old inner join on the two columns and then recreating the multiindex at the end.
Here are the sniplets:
df=df.reset_index()
df2=df2.reset_index()
df['year']=df['year'].apply(int)
df2['year']=df2['year'].apply(int)
df['week']=df['week'].apply(int)
df2['week']=df2['week'].apply(int)
result = pd.merge(df, df2, how='left', left_on= ['year','week'],right_on= ['year','week'])
result=result.set_index(['year', 'month','week','day'])
I'm using Python Pandas to merge two dataframe, like so:
new_df = pd.merge(df1, df2, 'inner', left_on='Zip_Code', right_on='Zip_Code_List')
However, I would like to do this ONLY where another column ('Business_Name') in df2 contains a certain value. How do I do this? So, something like, "When Business Name is Walmart, merge these two dataframes."
#you can filter on df2 first and then merge.
pd.merge(df1, df2.query("Business_Name' == 'Walmart'"), how='inner', left_on='Zip_Code', right_on='Zip_Code_List')