This method is used to designed for ordered data like time series data. Optionally perform group-wise merge.
Syntax : pandas.merge_ordered(left, right, on=None, left_on=None, right_on=None, left_by=None, right_by=None, fill_method=None, suffixes=(‘_x’, ‘_y’), how=’outer’)
Parameters :
- left : DataFrame
- right : DataFrame
- on : label or list
- left_on : label or list, or array-like
- right_on : label or list, or array-like
- left_by : column name or list of column names
- right_by : column name or list of column names
- fill_method : {‘ffill’, None}, default None
- suffixes : list-like, default is (“_x”, “_y”)
- how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘outer’
Returns : A DataFrame in which the merged DataFrame output type will be same as ‘left’, if it is a subclass of DataFrame.
Example 1 : merge ordered two Dataframe with same number of elements
Python3
# importing the moduleimport pandas as pd# creating the first DataFramedf1 = pd.DataFrame({ "date": ['2007-02-01', '2007-03-01', '2007-04-01', '2007-05-01', '2007-06-01'], "close": [12.08, 13.27, 14.27, 17.31, 17.43]})print("The first DataFrame")print(df1)# creating the second DataFramedf2 = pd.DataFrame({ "date": ['2007-01-01', '2007-02-01', '2007-03-01', '2007-04-01', '2007-05-01'], "close": [44.34, 43.68, 45.04, 48.27, 50.54]})print("The second DataFrame")print(df2)# merging the DataFramesprint("The merge_ordered DataFrame")df = pd.merge_ordered(df1, df2, on='date', suffixes=('_df1', '_df2'))print(df)# This code is contributed by rakeshsahni |
Output :
Example 2 : fills missing with previous value we use fill_method = ‘ffill’ ( Forward fill )
Python3
# importing the moduleimport pandas as pd# creating the first DataFramedf1 = pd.DataFrame({ "date": ['2007-02-01', '2007-03-01', '2007-04-01', '2007-05-01', '2007-06-01'], "close": [12.08, 13.27, 14.27, 17.31, 17.43]})print("The first DataFrame")print(df1)# creating the second DataFramedf2 = pd.DataFrame({ "date": ['2007-01-01', '2007-02-01', '2007-03-01', '2007-04-01', '2007-05-01'], "close": [44.34, 43.68, 45.04, 48.27, 50.54]})print("The second DataFrame")print(df2)# merging the DataFramesprint("The merge_ordered DataFrame")df = pd.merge_ordered(df1, df2, on='date', suffixes=( '_df1', '_df2'), fill_method='ffill')print(df)# This code is contributed by rakeshsahni |
Output :
Example 3 : we will use left_by parameter in which group left DataFrame by group columns and merge piece by piece with right DataFrame.
Python3
# importing the moduleimport pandas as pd# creating the first DataFramedf1 = pd.DataFrame( { "key": ["k1", "k3", "k5", "k1", "k3", "k5"], "value1": [1, 2, 3, 1, 2, 3], "gp": ["g1", "g1", "g1", "g2", "g2", "g2"] })df1print("The first DataFrame")print(df1)# creating the second DataFramedf2 = pd.DataFrame({"key": ["k2", "k3", "k4"], "value2": [1, 2, 3]})print("The second DataFrame")print(df2)# merging the DataFramesprint("The merge_ordered DataFrame")df = pd.merge_ordered(df1,df2,fill_method='ffill',left_by="gp")print(df)# This code is contributed by rakeshsahni |
Output :
