in-place

I have a json file that needs to be updated on a certain condition. Sample json { "Actions" : [ { "value" : "1", "properties" : { "name" : "abc", "age" : "2", "other ": "test1" } }, { "value" : "2", "properties" : { "name" : "def", "age" : "3", "other" : "test2" } } ] } I am writing a script that makes use of Jq to match a value and update, as shown below cat sample.json | jq '.Actions[] | select (.properties.age == "3") .properties.other = "no-test"' Output (printed to terminal) { "value": "1", "properties": { "name": "abc", "age": "2", "other ": "test1" } } { "value": "2", "properties": {

I have the following code which is attempting to normalize the values of an m x n array (It will be used as input to a neural network, where m is the number of training examples and n is the number of features). However, when I inspect the array in the interpreter after the script runs, I see that the values are not normalized; that is, they still have the original values. I guess this is because the assignment to the array variable inside the function is only seen within the function. How can I do this normalization in place? Or do I have to return a new array from the normalize function?

I'd like edit a file with sed on OS X. I'm using the following command: sed 's/oldword/newword/' file.txt The output is sent to the terminal. file.txt is not modified. The changes are saved to file2.txt with this command: sed 's/oldword/newword/' file1.txt > file2.txt However I don't want another file. I just want to edit file1.txt . How can I do this? I've tried the -i flag. This results in the following error: sed: 1: "file1.txt": invalid command code f whittle You can use the -i flag correctly by providing it with a suffix to add to the backed-up file. Extending your example: sed -i.bu 's

In the pandas library many times there is an option to change the object inplace such as with the following statement... df.dropna(axis='index', how='all', inplace=True) I am curious what is being returned as well as how the object is handled when inplace=True is passed vs. when inplace=False . Are all operations modifying self when inplace=True ? And when inplace=False is a new object created immediately such as new_df = self and then new_df is returned? When inplace=True is passed, the data is renamed in place (it returns nothing), so you'd use: df.an_operation(inplace=True) When inplace

I have reported this as an issue on pandas issues . In the meanwhile I post this here hoping to save others time, in case they encounter similar issues. Upon profiling a process which needed to be optimized I found that renaming columns NOT inplace improves performance (execution time) by x120. Profiling indicates this is related to garbage collection (see below). Furthermore, the expected performance is recovered by avoiding the dropna method. The following short example demonstrates a factor x12: import pandas as pd import numpy as np inplace=True %%timeit np.random.seed(0) r,c = (7,3) t =