You need to select that column:

In [41]:
df.loc[df['First Season'] > 1990, 'First Season'] = 1
df

Out[41]:
                 Team  First Season  Total Games
0      Dallas Cowboys          1960          894
1       Chicago Bears          1920         1357
2   Green Bay Packers          1921         1339
3      Miami Dolphins          1966          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers          1950         1003

So the syntax here is:

df.loc[<mask>(here mask is generating the labels to index) , <optional column(s)> ]

You can check the docs and also the 10 minutes to pandas which shows the semantics

EDIT

If you want to generate a boolean indicator then you can just use the boolean condition to generate a boolean Series and cast the dtype to int this will convert True and False to 1 and 0 respectively:

In [43]:
df['First Season'] = (df['First Season'] > 1990).astype(int)
df

Out[43]:
                 Team  First Season  Total Games
0      Dallas Cowboys             0          894
1       Chicago Bears             0         1357
2   Green Bay Packers             0         1339
3      Miami Dolphins             0          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers             0         1003

What I want to achieve: Condition: where column2 == 2 leave to be 2 if column1 < 30 elsif change to 3 if column1 > 90

This can be simplified into where (column2 == 2 and column1 > 90) set column2 to 3. The column1 < 30 part is redundant, since the value of column2 is only going to change from 2 to 3 if column1 > 90.

In the code that you provide, you are using pandas function replace, which operates on the entire Series, as stated in the reference:

Values of the Series are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

This means that for each iteration of for x in filter1 your code performs global replacement, which is not what you want to do - you want to update the specific row of column2 that corresponds to x from column1 (which you are iterating over).

the problem is 2 does not change to 3 where column1 > 90

This is truly strange. I would expect the code you provided to have changed every instance of 2 in column2 to 3 as soon as it encountered an x >= 30, as dictated by your code conditional statement (the execution of the else branch). This discrepancy may stem from the fact that you are assigning to column2 the result of global replacement performed on the column Output (the contents of which are unknown). In any case, if you want your program to do something under a specific condition, such as x > 90, it should be explicitly stated in the code. You should also note that the statement data['column2'] = data['column2'].replace([2], [2]) achieves nothing, since 2 is being replaced with 2 and the same column is both the source and the destination.

What you could use to solve this particular task is a boolean mask (or the query method). Both are explained in an excellent manner in this question.

Using a boolean mask would be the easiest approach in your case:

mask = (data['column2'] == 2) & (data['column1'] > 90)
data['column2'][mask] = 3

The first line builds a Series of booleans (True/False) that indicate whether the supplied condition is satisfied. The second line assigns the value 3 to those rows of column2 where the mask is True.

It's not something you'd really use .apply for. You would use boolean indexing, e.g. df['A'].str.contains('foo') would give you a Series of True/False values. You can then use .loc to set column(s) to a particular value for the True rows: df.loc[df['A'].str.contains('foo'), 'B'] = 'bar'

You almost did it. 🙂 = Table.ReplaceValue( #"Changed Type", each if [PARK_ID] = 88 then [Campground_Name] else false, each "Chain Lakes South", Replacer.ReplaceValue, {"Campground_Name"} ) Before:After:Full code:let Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMlTSUQrOz01VyM9JUSguKU1LU4rViVYywi5sjF3YwgK7uKUlCepjAQ==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [PARK_ID = _t, Campground_Name = _t]), #"Changed Type" = Table.TransformColumnTypes(Source,{{"PARK_ID", Int64.Type}, {"Campground_Name", type text}}), #"Replaced Value" = Table.ReplaceValue( #"Changed Type", each if [PARK_ID] = 88 then [Campground_Name] else false, each "Chain Lakes South", Replacer.ReplaceValue, {"Campground_Name"} ) in #"Replaced Value"

You can assign multiple columns to the same value at the same time. >>> df foo bar baz 0 1 4 7 1 2 5 8 2 1 6 9 >>> df.loc[ df['foo'] == 1, ['baz', 'bar']] = '-' >>> df foo bar baz 0 1 - - 1 2 5 8 2 1 - - It's .loc[rows, columns] = value >>> rows = df['foo'] == 1 >>> columns = 'bar', 'baz' >>> df.loc[rows, columns] = '-' >>> df foo bar baz 0 1 - - 1 2 5 8 2 1 - -

df = pd.merge(df1, df2, left_on='Project Name', right_on='Name of Project', how='left') cond = df['Project Owner'] == 'Default' df.loc[cond, 'Project Owner'] = df.loc[cond, 'Owner of Project'] There's probably an easier solution, but I can't think of it. Edit: Actually there is something a little easier. Instead of using the condition and loc, you can use combine_first to combine the non NA values between the two columns df = pd.merge(df1, df2, left_on='Project Name', right_on='Name of Project', how='left') df['Project Owner'] = df['Project Owner'].combine_first(df['Owner of Project']) Use this if you want to drop the two new columns. df = df.drop(columns=['Name of Project', 'Owner of Project'])

A clean syntax for this kind of "find and replace" uses a dict, as

df.Num_of_employees = df.Num_of_employees.replace({"10-Jan": "1-10",
                                                   "Nov-50": "11-50"})