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80 lines
1.8 KiB
Python
80 lines
1.8 KiB
Python
"""
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author: Wojciech Janota
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Laboratory: Lab 2, ex. 3
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"""
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import pandas as pd
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import numpy as np
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input_data = pd.read_csv('Diet_R.csv', sep=',')
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input_data["gender"].replace(" ", np.NaN, inplace=True)
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input_data["gender"].fillna(input_data["gender"].mode()[0], inplace=True)
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summary_stats = input_data.describe()
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median = summary_stats.loc['50%', :]
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first_quartile = summary_stats.loc['25%', :]
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third_quartile = summary_stats.loc['75%', :]
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mean = summary_stats.loc['mean', :]
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std_dev = summary_stats.loc['std', :]
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print("Statistics")
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# Print the results
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print("Median:\n", median)
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print("\nFirst Quartile:\n", first_quartile)
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print("\nThird Quartile:\n", third_quartile)
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print("\nMean:\n", mean)
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print("\nStandard Deviation:\n", std_dev)
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with open("result.txt", "w") as output_file:
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result = f"""
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\t\tGeneral statistics:
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\tMedian:
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{median}
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\tFirst Quartile:
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{first_quartile}
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\tThird Quartile:
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{third_quartile}
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\tMean:
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{mean}
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\tStd. deviation:
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{std_dev}
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"""
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output_file.write(result)
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grouped_stats = input_data.groupby('gender').describe()
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print("Statistics grouped by gender")
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# Extract specific values
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median = grouped_stats.xs(key='50%', level=1, axis=1)
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first_quartile = grouped_stats.xs(key='25%', level=1, axis=1)
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third_quartile = grouped_stats.xs(key='75%', level=1, axis=1)
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mean = grouped_stats.xs(key='mean', level=1, axis=1)
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std_dev = grouped_stats.xs(key='std', level=1, axis=1)
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# Print the results
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print("Median:\n", median)
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print("\nFirst Quartile:\n", first_quartile)
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print("\nThird Quartile:\n", third_quartile)
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print("\nMean:\n", mean)
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print("\nStandard Deviation:\n", std_dev)
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with open("result.txt", "a") as output_file:
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result = f"""
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\n\n
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\t\tStatistics per gender:
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\tMedian:
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{median}
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\tFirst Quartile:
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{first_quartile}
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\tThird Quartile:
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{third_quartile}
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\tMean:
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{mean}
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\tStd. deviation:
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{std_dev}
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"""
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output_file.write(result)
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