Here I have a 1D array: And the sum of all elements in the array should be 75491328*8*8 = 4831444992. However, when I use np.sum, I get a different output. That’s what happens on my Jupyter Notebook using the latest version of Numpy. But when I use Jupyter Notebook of Coursera using old version 1.18.4 of Numpy, everything is fine.
Tag: numpy
Create new column in Pandas and fill down with a specific value
I have a dataset, df, where I would like to create a new column to my dataset and fill down this column with a specific value Data Desired Doing However, this is not actually creating the new column. Any suggestion is appreciated. Answer Simply try: df[‘plan’] = ’21’ Or df[‘plan’] = 21 if an integer type is wanted. Pandas will
Is there a simple way to remove “padding” fields from numpy.dtype.descr?
Context Since numpy version 1.16, if you access multiple fields of a structured array, the dtype of the resulting array will have the same item size as the original one, leading to extra “padding”: The new behavior as of Numpy 1.16 leads to extra “padding” bytes at the location of unindexed fields compared to 1.15. You will need to update
seperate array from large array in numpy by column condition
check if values of a,b are 1 ,2 and c,d are 3,4 then print it what i am currently doing is but it prints all the rows where the 1st column is 1 Answer You can slice your array and then use row equality checks: BTW, it is always a good idea to make an example that can be reproduced
Create calculated field within dataset using Python
I have a dataset, df, where I would like to create columns that display the output of a subtraction calculation: Data Desired Doing I am looking for a more elegant way that provides the desire output. Any suggestion is appreciated. Answer We can perform 2D subtraction with numpy: Benefit here is that, no matter how many p_ columns there are,
np.select pandas dataframe based on column of prefix and values
So I have two dataframes main_df, about 800 rows description category ABCD ONE XYZ THREE ABC QWE keyword_df, it is about 50 rows keyword category AB FIVE What I’m trying to achieve = main_df description category ABCD ONE XYZ THREE ABC FIVE QWE 0 conditions = [(main_df[‘Description’].str.startswith(‘AB’)) & (main_df[‘category’).isnull()] values = keyword_df[‘category’].tolist() main_df[‘category’] = np.select(conditions, values) I was able to
How to convert 0-1 image float array to 0-255 int array
Plot results in: Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). and only displays an image in the first subplot Used image Answer The issue is photo*=255 is still an array of floats. Look at the photo array. Add photo = photo.astype(int) after photo*=255. X in .imshow should be
How to fillna in pandas dataframe based on pattern like in excel dragging?
I have dataframe which should be filled by understanding rows understanding like we do in excel. If its continious integer it fill by next number itself. Is there any function in python like this? output required: I tried df.interpolate(method=’krogh’) #it fill 1,2,3,4,5,6 but incorrect others. Answer Here is my solution for the specific use case you mention – The code
How work to with an array of arrays and how initialize multiple arrays in Numpy?
I have to write an ABM (agent-based model) project in Python and I need to initialize 50 agents which will each contain a different set of numbers. I cannot use a matrix of 50 rows because each agent (each row) can have a different quantity of elements so the vector of each agent has not the same length: when certain
How to subtract 1d array from 2d array’s rows in Numpy
What I want to do is to subtract 1d array from other 2d array’s rows. I found, in some case, result was wrong and confused me. Below code worked good as I wanted. Then I got following as expected. However, when I change the array “lstave” to [1, 2, 3, 4, 5] and subtract, I got following. I cannot understand