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 two CSV files, CSV_Cleaned: It has 891 rows and CSV_Uncleaned: this one has 945 rows, I wish to get only those rows from CSV_Uncleaned whose index value matches with CSV_Cleaned. How do I do it?
NOTE: My data frame has no column named 'index', I am talking about the index values that are automatically generated on the left of the 1st column.
assuming the column of interest is called "index" on the csv files, you can do this using merge
df1 = pd.read_csv('CSV_cleaned.csv')
df2 = pd.read_csv('CSV_Uncleaned.csv')
df = df1.merge(df2, left_on='index', right_on='index', how='left')
in case you already have DataFrames that need to be merged by their index:
df = df1.merge(df2, left_index=True, right_index=True, how='left')
I am trying to merge three dataframes based on their row indices. However, the on attribute will not take index as an option. Is there any better ways to merge the dataframes without having to write the row indices to each dataframe as a column?
from functools import reduce
dfs = [result_eu_SpeciesNameGenuine, result_ieu_SpeciesNameGenuine, result_cosine_SpeciesNameGenuine]
df_final = reduce(lambda left,right: pd.merge(left,right,on=index), dfs)
df_final
Try this using pd.DataFrame.join per documentation other can be a list of dataframes:
dfs[0].join(dfs[1:])
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 working on a way to transform sequence/genotype data from a csv format to a genepop format.
I have two dataframes: df1 is empty, df1.index (rows = samples) consists of almost the same as df2.index, except I inserted "POP" in several places (to specify the different populations). df2 holds the data, with Loci as columns.
I want to insert the values from df2 into df1, keeping empty rows where df1.index = 'POP'.
I tried join, combine, combine_first and concat, but they all seem to take the rows that exist in both df's.
Is there a way to do this?
It sounds like you want an 'outer' join:
df1.join(df2, how='outer')