I have a dataframe like as below I would like to do the below a) Attach the location column from key_df to data_df based on two fields – p_id and company So, I tried the below But this resulted in error like below KeyError: “None of [Index([‘p_id’,’company’], dtype=’object’)] are in the [columns]” How can I map based on multiple index
Tag: series
Pandas data frame index
if I have a Series But, I need a standard index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], with index[4, 7, 8] values equal to zeros. So I expect the updated series will be How should I update the series? Thank you in advance! Answer Try this: Output:
Python: How to filter a Pandas DataFrame using Values from a Series?
Context I am currently processing some data and encountered a problem. I would like to filter a Pandas DataFrame using Values from a Series. However, this always throws the following Error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). Code Question Does anyone have an idea what’s this error means and how
How to store High or Low values (trading)
I would like to develop a code which add a Series to my DataFrame; the Series should store the lowest value of the Close until a new low is reached. When a new low is reached a new value should appear in the Series. The starting code is: So, for example, if the most recent low of EURUSD is 1.1000,
Data type assigned inside nested for loop isn’t as expected
I get the error: AttributeError: ‘float’ object has no attribute ‘lower’ When trying to compile this triple nested for loop: df_row_list is a list of 18 series. I am trying to iterate through it and comb through the data. How do I assign the str data type to row_item_data so that I can use the .lower() attribute? This is kinda
pandas rename multiple columns using regex pattern
I have a dataframe like as shown below I would like to remove the keyword US – from all my column names I tried the below but there should be better way to do this But my real data has 70 plus columns and this is not efficient. Any regex approach to rename columns based on regex to exclude the
How to reverse a pandas series
I have a pandas series that must be flipped upside-down before I concatenate it to the main DataFrame. I can easily flip it with myseries = myseries.iloc[::-1] But when I attach it to the main DataFrame, it attaches the default series and not the flipped version. Why doesn’t the flipped series stay in place? EDIT: So my guess is that
Apply multiple criteria to select current and prior row – Pandas
I have a dataframe like as shown below I would like to select rows based on the criteria below criteria 1 – pick all rows where source-system = I criteria 2 – pick prior row (n-1) only when source-system of (n-1)th is O and diff is zero. This criteria 2 should be applied only when nth row has source-system =
Adding multiple constant values in a pandas dataframe column
I would like to know how to add multiple constant values of different lengths into a dataframe column. I know that we can add a single constant value (for example: 5) to a data frame column ‘A’ like this: But I want to have the dataframe something like the table below. As you can see, I need three 5s, two
How to efficiently combine multiple pandas columns into one array-like column?
It is easy to create (or load) a DataFrame with something like an object-typed column, as so: I am currently in the position where I have, as separate columns, values that I am required to return as a single column, and need to do so quite efficiently. Is there a fast and efficient way to combine columns into a single