It’s because you have: def first_function(x): try: return do_something_else() without a matching except block after the try: block. Every try must have at least one matching except. See the Errors and Exceptions section of the Python tutorial.
You can use clip. Apply to all columns of the data frame: df.clip(upper=15) Otherwise apply to selected columns as seen here: df.clip(upper=pd.Series({‘a’: 15}), axis=1)
import sys sys._getframe(number) The number being 0 for the current frame and 1 for the frame up and so on up. The best introduction I have found to frames in python is here However, look at the inspect module as it does most common things you want to do with frames.
Found a better solution: class RequiredFormSet(BaseFormSet): def __init__(self, *args, **kwargs): super(RequiredFormSet, self).__init__(*args, **kwargs) for form in self.forms: form.empty_permitted = False Then create your formset like this: MyFormSet = formset_factory(MyForm, formset=RequiredFormSet) I really don’t know why this wasn’t an option to begin with… but, whatever. It only took a few hours of my life to figure … Read more
[:] Shallow copies the list, making a copy of the list structure containing references to the original list members. This means that operations on the copy do not affect the structure of the original. However, if you do something to the list members, both lists still refer to them, so the updates will show up … Read more
You can read the content of each file directly into the write method of the output file handle like this: import glob read_files = glob.glob(“*.txt”) with open(“result.txt”, “wb”) as outfile: for f in read_files: with open(f, “rb”) as infile: outfile.write(infile.read())
There is already a way provided by requests to access links header response.links It returns the dictionary of links header value which can easily parsed further using response.links[‘next’][‘url’] to get the required values.
there is even a shorter one 🙂 print df.groupby(‘name’).describe().unstack(1) Nothing beats one-liner: In [145]: print df.groupby(‘name’).describe().reset_index().pivot(index=’name’, values=”score”, columns=”level_1″)
To use UMAP you need to install umap-learn not umap. So, in case you installed umap run the following commands to uninstall umap and install upam-learn instead: pip uninstall umap pip install umap-learn And then in your python code make sure you are importing the module using: import umap.umap_ as umap Instead of import umap
I can reproduce this on my Q6600 (Python 2.6.2); increasing the range to 100000000: (‘+=’, 11.370000000000001) (‘-=’, 10.769999999999998) First, some observations: This is 5% for a trivial operation. That’s significant. The speed of the native addition and subtraction opcodes is irrelevant. It’s in the noise floor, completely dwarfed by the bytecode evaluation. That’s talking about … Read more