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I want to generate 5 experimental data sets for my dataset (heart.csv).
https://www.kaggle.com/ronitf/heart-disease-uci. Then for each experimental data set, I conduct 5 fold cross-validation.
Let's say you have read all the data into a dataframe called df, then try this:
import numpy as np
#Shuffle data
df_shuffled_1 = df.sample(frac=1)
df_shuffled_2 = df_shuffled_1.sample(frac=1)
...
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import pandas as pd
Data = pd.open_csv('file name')
Data = isnull().sum()
I know that here we are testing each cell if it were empty or not; then, return the summation of empty ones.
Is there any other ways to re-write the statement?
Can we store the True results in a data structure, add them using a loop, and return the result?
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I have one data frame in python. How can I calculate the 3sigma value for each column of my data frame using python? please help me with this.
The command you're looking for is df.std() * 3
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I would like to make a number array with numpy based on user inputs then find the mean median and mode of this array.
Here you go:
import numpy as np
array = np.asarray([int(i) for i in input().split()])
print(array.mean())
print(array.mode())
print(array.median())
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I would like to shuffle each 5 rows together without changing the order in that group.
Pulling from: https://stackoverflow.com/a/44729807/7253453
You can achieve this by
import random
n = 5 #chunk row size
list_df = [df[i:i+n] for i in range(0,df.shape[0],n)]
random.shuffle(list_df)
df = pd.concat(list_df)
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consider a vector with hourly observations, the data is updated every 12 hours.
with R, I could do
ts(vector_with_R, frequency=12)
In statsmodels "freq" controls the units for the time series not the "data window". How can I change the "data window".
For this purpose you need pandas - most popular python package for working with timeseries and another analytic data. Statsmodels work with it:
import pandas as pd
t=pd.TimeSeries(range(10), index=pd.date_range(start='2010-10-10 06:00:00', periods=10, freq='H'))