Let's assume that you have a mapping such as:

mapping = {"old_name_1" : "new_name_1", "old_name_2" : "new_name_2"}

Pandas version < 1.4.3

Given the previous documentation from pandas, the default value on axis parameter is 0 (meaning index).

Thus, changing your command to:

df = df.rename(columns = mapping, axis = 1)

, where axis equal to 1 means columns, will work as expected.

Also, you can use the inplace parameter so you won't have to re-set your DataFrame.

df.rename(columns = mapping, axis = 1, inplace = True)

Pandas version >= 1.4.3

(thanks @Gordon for the heads-up)

You can just use:

df = df.rename(columns = mapping)

or

df.rename(columns = mapping, inplace = True)

With the new update (see documentation), pandas understand that when you set a mapping to columns parameter, you mean to change the value of columns (as it is logical to happen); thus, the axis parameter is unnecessary.

Could you try this instead? Assuming I've understood what you're trying to do, which is rename a column called (1, 'Snd_Mer_Vol_Probability') to Snd_Mer_Vol_Probability

Snd_Mer_Vol_Output.rename(columns={"(1, 'Snd_Mer_Vol_Probability')": 'Snd_Mer_Vol_Probability'},inplace=True)

EDIT:

You actually need:

Snd_Mer_Vol_Output.rename(columns={(1, 'Snd_Mer_Vol_Probability'): 'Snd_Mer_Vol_Probability'},inplace=True)

As your .columns output below shows that the column name is a tuple and not a string, so it doesn't need quotes (double or otherwise) around it, as you can see I've done an example myself:

df = pd.DataFrame({(1,'hello'):[1],'test':[2]})
print(df)

>>   test  (1, hello)
>>      2           1

df.rename(columns={(1,'hello'):'testing2'},inplace=True)
print(df)

>>   test   testing2
>>      2          1

The rename method in pandas generally operates on the labels of specific levels when dealing with a MultiIndex, rather than treating the full column tuples as single keys. Because of this, passing a dictionary of tuples often fails to match the columns as you intend.

To solve this, the most reliable approach is to rebuild the index using a list comprehension or map to apply your dictionary, and then assign it back to df.columns.

When df.rename(columns=...) is called on a MultiIndex, pandas attempts to align the dictionary keys with the labels of the index levels, not the composite tuples (pairs).Since your keys are tuples and the level labels are individual strings, no match is found, and nothing changes.

import pandas as pd

cols = pd.MultiIndex.from_tuples([
    ('Group_A', 'Metric_1'), 
    ('Group_A', 'Metric_2'), 
    ('Group_B', 'Metric_1'), 
    ('Group_B', 'Metric_2')
])
df_test = pd.DataFrame([
    [10, 20, 30, 40], 
    [50, 60, 70, 80]
], columns=cols)

test_map = {
    ('Group_A', 'Metric_1'): ('Group_A', 'Current_1'),
    ('Group_A', 'Metric_2'): ('Group_A', 'Current_2'),
    ('Group_B', 'Metric_1'): ('Group_B', 'Current_1')
}

# --- Solution ---
# Create a new list of columns by looking up each tuple in your map;
# if it's not in the map, keep the original tuple.
new_columns = [test_map.get(col, col) for col in df_test.columns]

# Assign the new columns back to the DataFrame
df_test.columns = pd.MultiIndex.from_tuples(new_columns)

print("--- Check Results ---")
print(df_test.columns.tolist())