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The simplest concatenation with concat() is by passing a list of DataFrames, for example [df1, df2] . And by default, it is concatenating vertically along the axis 0 and preserving all existing indices. If you want the concatenation to ignore existing indices, you can set the argument ignore_index=True .
Using loc[] to Append The New List to a DataFrame. By using df. loc[index]=list you can append a list as a row to the DataFrame at a specified Index, In order to add at the end get the index of the last record using len(df) function.
Pandas merge() function is used to merge multiple Dataframes. We can use either pandas. merge() or DataFrame. merge() to merge multiple Dataframes.
By using pandas. concat() you can combine pandas objects for example multiple series along a particular axis (column-wise or row-wise) to create a DataFrame. concat() method takes several params, for our scenario we use list that takes series to combine and axis=1 to specify merge series as columns instead of rows.
Given that all the dataframes have the same columns, you can simply concat them:
import pandas as pd
df = pd.concat(list_of_dataframes)
If the dataframes DO NOT all have the same columns try the following:
df = pd.DataFrame.from_dict(map(dict,df_list))
You also can do it with functional programming:
from functools import reduce
reduce(lambda df1, df2: df1.merge(df2, "outer"), mydfs)
Just to add few more details:
Example:
list1 = [df1, df2, df3]
import pandas as pd
Row-wise concatenation & ignoring indexes
pd.concat(list1, axis=0, ignore_index=True)
Note: If column names are not same then NaN would be inserted at different column values
Column-wise concatenation & want to keep column names
pd.concat(list1, axis=1, ignore_index=False)
If ignore_index=True, column names would be filled with numbers starting from 0 to (n-1), where n is the count of unique column names
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