I would like to understand how “style” works in python, I always have the same error : AttributeError: ‘Styler’ object has no attribute …. for now, I manage to have : the first row in yellow but not the conditional formatting (df_x0) and the conditional formatting blue and orange cells but the first row disappear because I am obligate to

# Tag: apply

## Apply a function including if to each row of a dataframe in pandas without for loop

Given a dataframe, I want to get the nonzero values of each row and then find the minimum of absolute values. I want to have a user defined function that does this for me. Also, I do not want to use any for loop since the data is big. My try I get ValueError: The truth value of a Series

## Replace value based on a corresponding value but keep value if criteria not met

Given the following dataframe, INPUT df: Cost_centre Pool_costs 90272 A 92705 A 98754 A 91350 A Replace Pool_costs value with ‘B’ given the Cost_centre value but keep the Pool_costs value if the Cost_centre value does not appear in list. OUTPUT df: Cost_centre Pool_costs 90272 B 92705 A 98754 A 91350 B Current Code: This code works up until the else

## Convert a list of strings in pandas into a list of date, and filter the value accordingly

Now I have a dataframe similar as follows What I am trying to do is to Convert all the values in the list to date Filter the value which are only later than 2018-01-01 In the first step, what I tried to do is to use a apply function so that I can cover all elements in the list: However,

## Make list after groupby in pandas using apply() function

I have this dataframe: My goal is to keep tracking the values in column2, based on the letters of column1 separated by(:), the output should look like this: What’s the most pythonic way to do this: At the moment I’m able to group by the column 1 and I’m trying to use the apply() function, but I do not know

## Alternative to apply function in pandas

I would like to execute this simple transformation in a more efficient way. df[“amount”] = df.apply( lambda row: 500 if row.amount > 500 else row.amount, axis=1 ) Any ideas?

## How to vectorize groupby and apply in pandas?

I’m trying to calculate (x-x.mean()) / (x.std +0.01) on several columns of a dataframe based on groups. My original dataframe is very large. Although I’ve splitted the original file into several …

## How to print the current row number when using .apply on DataFrame

I’ve seen this question for R, but not for python. Basically, I have a large DataFrame where I apply a function row-wise. It takes a very long time to run and I hoped to put a print statement to show where I am. I put together an example of what I would like to do. […]

## AttributeError: ‘PandasExprVisitor’ object has no attribute ‘visit_Ellipsis’, using pandas eval

I have a series of the form: s 0 [133, 115, 3, 1] 1 [114, 115, 2, 3] 2 [51, 59, 1, 1] dtype: object Note that its elements are strings: s[0] ‘[133, 115, 3, 1]’ I’m trying to use pd….

## Python/Pandas: If Column has multiple values, convert to single row with multiples values in list

In my DataFrame, I have many instances of same AutoNumber having different KeyValue_String. I would like to convert these instances to a single row where the KeyValue_String is a list comprised of the multiple unique values. The desired output would look like this, except I want to keep all of the other columns Answer If I understand correctly, you could