From junior to geek the road is sleek
Hey guys, if you stumble upon this error, most probably you have conflicting modules installed in your pip or other package management tool. And by the way, PyDrive is not supported anymore for quite a long time, so it’s better to install PyDrive2 pip install PyDrive2 PyDrive2 an actively maintained fork of PyDrive. If you … Read more
The easiest and pythonic way to do this is the following: for idx, item in enumerate(my_list): print(idx) print(item)
The simple code would be: df.index[condition] so something like this: df.index[df[‘column1’] == True].tolist() .tolist() is used when there are multiple values match the condition
.item() will do the job: p1 = df[df[“column2”]==df.column2.max()].column1.item() print(p1) This way you can extract the actual value from pandas dataframe and store it in variable for later use.
Hello. I have been using np.arrange for a long time but only now I have noticed that this built-in function doesn’t include the last maximum value. Like for example: step = 2 print(np.arange(0, 26, step)) results in [0 2 4 6 8 10 12 14 16 18 20 22 24] So how do we make … Read more
Here’s my example: params = { ‘param1’: [[25, 30, 35],[-25, -30, -35]], ‘param2’: [[20, 25, 30],[-20, -25, -30]] } for key, value in paramsNEW.items(): print( key, value[0], value[1] ) for v in value: print(v) for key, value in paramsNEW.items(): print(key, [(v1,v2) for v1 in value[0] for v2 in value[1]])
In a normal njit function setting error_model=”numpy” does exactly this. There are also significant speedups possible by setting this option. So: @njit(error_model=”numpy”) If you are wandering what Numba is, read more about Numba here. It’s a really cool thing, I highly recommend getting familiar with it.
from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0,1)) a = df.to_numpy() a = scaler.fit_transform(a)
Here’s a good example to understand quantiles in python: import numpy as np d = [1, 1.2, 1.5, 2, 6, 7, 22, 3] q = 0.99 qr = np.quantile(d, q) print(f”{q*100}% less than {qr}”)
Here we calculate 0.9th quantile of each column in our dataframe: q = 0.9 for column in df: qr = df[column].quantile(q) print(f”{q*100}% are lower than {qr}”) Here’s a good example to understand quantiles.