I have a dataframe, In that if the value is starting with letter “A” i’m styling it in red color, now i need to send it as html table in mail but when i execute it its coming without that styling, below is the code i tried please help. please check the image for df style enter image description here
Tag: dataframe
Replacing values in a pandas dataframe taking row number into account
I have this dataframe taken from an excel file and I want to replace the value column with a string that contains the row number (+1 and with an F so it reference another cell in excel) if the state is “no”. I had no problem replacing some values in the comment column with but I dont know how take
How do I loop through a pandas dataframe, check to see if the data type of each column is a float number, then replace null values with mean?
I am trying to iterate through a pandas Dataframe which has columns with different data types, and replace them with different data types based on their null values. This code did not work as the null values are not replaced in the dataframe. Answer fillna() doesn’t normaly edit your dataframe. you have 2 ways: or:
Get index of row where pandas column contains regex
I am checking to see if a pandas column matches a pre-defined regex, using .any() to get the first match if found. However, I need to return the index/row where this match occurred so that I can get the value of another column in that row. I have the below to check where the reg_ex pattern exists in df[‘id_org’] Once
lookup for a value in one column and return from another column’s corresponding row in pands
I know it is a fundamental question, but I couldn’t solve it. Any help will be appreciated My list1 has around 1059 values, list2 has around 7 values. I want to check values in list1 against values in list2, if matches append ‘x’ to list variable, if not matches append ‘y’ to same list variable. So at the end I
Python: How to explode column of dictionaries into columns with matching keys?
I have a column in pandas dataframe that has the following structure (see example). I think I have a nested dictionary in a single column, and I want each key to have it’s own column. I want all the matching keys to be the same column. Run the examples for more details I want to explode the dataframe so that
how to get .value_count and values in single data frame
This is my sample csv When I do .value_counts() I get I want to get This is my current attempt This does not concat the two df properly and does not have the ID Any suggestions? Answer You can use a groupby.agg in place of value_counts: Output:
how to use multiple when conditions in pyspark for updating column values
I am looking for a solution where we can use multiple when conditions for updating a column values in pyspark. I am currently trying to achieve a solution when we have multiple conditions in spark how we can update a column. I have one dataframe in which we have three columns DATE, Flag_values, salary: After this I have to update
How to remowe a string up to a specific character (Python/pandas)?
I have the DataFrame: How I can cut values that get the next result, which you can see in the df[‘name_2] column: enter image description here Answer You can use urllib.parse module to parse those URLs.
How to filter subcategories of rows from one column, based on counts in second column
Sorry it’s a bit complicated, but lets say I have a very long table of IDs and Fruits: ID Fruit 1 Apple 2 Banana 4 Orange … … 3 Banana 1 Orange The ID may be repeated several times in the table and the fruit may also be repeat several times. For example, in the whole dataframe, ID #1 can