Python | pandas.merge_ordered() function

Last Updated : 30 Nov, 2022

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 module
import pandas as pd

# creating the first DataFrame
df1 = 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 DataFrame
df2 = 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 DataFrames
print("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 module
import pandas as pd

# creating the first DataFrame
df1 = 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 DataFrame
df2 = 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 DataFrames
print("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 module
import pandas as pd

# creating the first DataFrame
df1 = pd.DataFrame(
    {
        "key": ["k1", "k3", "k5", "k1", "k3", "k5"],
        "value1": [1, 2, 3, 1, 2, 3],
        "gp": ["g1", "g1", "g1", "g2", "g2", "g2"]
    }
)
df1
print("The first DataFrame")
print(df1)

# creating the second DataFrame
df2 = pd.DataFrame({"key": ["k2", "k3", "k4"], "value2": [1, 2, 3]})

print("The second DataFrame")
print(df2)

# merging the DataFrames
print("The merge_ordered DataFrame")

df = pd.merge_ordered(df1,df2,fill_method='ffill',left_by="gp")

print(df)
# This code is contributed by rakeshsahni