Complete Python Topics for Data Analysis: https://t.me/sqlspecialist/548
6. Statistical Analysis with Python:
1. Descriptive Statistics:
- Measures of Central Tendency:
- Calculate mean, median, and mode to understand the central value of a dataset.
mean_value = df['column'].mean()
median_value = df['column'].median()
mode_value = df['column'].mode()
- Measures of Dispersion:
- Assess variability with measures like standard deviation and range.
std_dev = df['column'].std()
data_range = df['column'].max() - df['column'].min()
2. Inferential Statistics and Hypothesis Testing:
- T-Tests:
- Compare means of two groups to assess if they are significantly different.
from scipy.stats import ttest_ind
group1 = df[df['group'] == 'A']['values']
group2 = df[df['group'] == 'B']['values']
t_stat, p_value = ttest_ind(group1, group2)
- ANOVA (Analysis of Variance):
- Assess differences among group means in a sample.
from scipy.stats import f_oneway
group1 = df[df['group'] == 'A']['values']
group2 = df[df['group'] == 'B']['values']
group3 = df[df['group'] == 'C']['values']
f_stat, p_value = f_oneway(group1, group2, group3)
- Correlation Analysis:
- Measure the strength and direction of a linear relationship between two variables.
correlation = df['variable1'].corr(df['variable2'])
Statistical analysis is crucial for drawing meaningful insights from data and making informed decisions. To learn more, you can read this book on statistics.
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