In this step-by-step tutorial, you'll learn the fundamentals of descriptive statistics and how to calculate them in Python. You'll find out how to describe, summarize, and represent your data visually using NumPy, SciPy, pandas, Matplotlib, and the built
Using the convenient pandas .quantile() function, we can create a simple Python function that takes in our column from the dataframe and outputs the outliers: #create a function to find outliers using IQR def find_outliers_IQR(df):
Conditional formatting is a feature in pandas that allows you to format the cells based on some criteria. You can easily highlight the outliers, visualize trends, or emphasize important data points using it. The Styler object in pandas provides a convenient way to apply conditional formatting. Be...
In this step-by-step tutorial, you'll learn the fundamentals of descriptive statistics and how to calculate them in Python. You'll find out how to describe, summarize, and represent your data visually using NumPy, SciPy, pandas, Matplotlib, and the built
IQR is the difference between 75th percentile(Q3) and 25th percentile(Q1) in a dataset. The value outside the 1.5X of the IQR range is the outlier. Program to illustrate the removing of outliers in Python using Interquartile Range method ...
q3 = np.percentile(array, 75) #calculate interquartile range iqr = q3 - q1 #calculate upper and lower whisker boundary upper_boundary = q3 + 1.5*iqr lower_boundary = q1 - 1.5*iqr lower_boundary, upper_boundary We will now just filter our array and find if there are any records that ...
Now that we have a model, we can plug values into the screen-on time variable to predict how much the battery will drain. I candefine a Python functionfor this: defphone_battery_usage(minutes): return5.339232+0.201630* minutes To calculate usage for three hours or 180 minutes: ...
We can then calculate the cutoff for outliers as 1.5 times the IQR and subtract this cut-off from the 25th percentile and add it to the 75th percentile to give the actual limits on the data. 1 2 3 4 ... # calculate the outlier cutoff cut_off = iqr * 1.5 lower, upper = q25 -...
Simple box-plot reveals that movies that belong to a collection benefit from a higher Revenue as reflected by the median and the range (25,75 percentile), the orange (right) box-plot is more elevated. fig, ax= plt.subplots(figsize=(8,6)) ...