Summary statistics are numerical values that provide a brief summary of the features of a dataset. In Python, you can use various libraries such as NumPy, Pandas, and SciPy to calculate summary statistics for your data.
Here are some common summary statistics that can be calculated in Python:
1. Mean: The arithmetic average of all the values in the dataset.
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import numpy as np
data = [2, 4, 6, 8, 10]
mean = np.mean(data)
print(mean)
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2. Median: The middle value in a dataset when it is arranged in ascending or descending order.
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import numpy as np
data = [2, 4, 6, 8, 10]
median = np.median(data)
print(median)
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3. Mode: The most frequently occurring value in the dataset.
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from scipy import stats
data = [2, 4, 6, 6, 8, 8, 8, 10]
mode = stats.mode(data)
print(mode)
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4. Range: The difference between the highest and lowest values in the dataset.
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import numpy as np
data = [2, 4, 6, 8, 10]
range = np.max(data) - np.min(data)
print(range)
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5. Standard deviation: A measure of the spread of the data from the mean.
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import numpy as np
data = [2, 4, 6, 8, 10]
std_dev = np.std(data)
print(std_dev)
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These are just a few examples of the summary statistics that can be calculated in Python. Depending on the specific needs of your analysis, you may need to calculate other summary statistics as well.