1
2**financials_df.head()** displays the first few rows and can immediately flag missing data or anomalies.
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4**financials_df.tail()** shows you the end of the dataset, often revealing how recent the data is and whether it's been truncated.
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6
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8### Data Structure
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10An understanding of your DataFrame's structure is essential before diving into deeper analysis.
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12We can use **stocks_df.info()** to get a summary of the DataFrame, including the number of non-null entries and data types of each column. This can highlight if certain columns contain missing values that need to be addressed or if data types need conversion.
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14
Print a concise summary of the DataFrame
print(stocks_df.info())
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4### Descriptive Statistics
5
6Descriptive statistics provide a high-level summary of the attributes of your dataset
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8 - **stocks_df.describe()** gives a statistical summary for numerical columns, useful for a quick assessment of distribution and variability.
9 - Custom aggregations like **stocks_df ['** **GOOGL '].mean()** help
10 in understanding specific aspects like the average.
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12
Get a statistical summary
print(stocks_df.describe())
Find the average price for Google
print(stocks_df['GOOGL'].mean())
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4### Aggregation
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6For more specific summary statistics, you can use aggregation methods like mean(), median(), min(), max(), and sum():
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8
Calculate the average opening price
print(stocks_df['MSFT'].mean())
Find the maximum closing price
print(stocks_df['MSFT'].max())
1The result
2The result will be 61.96290836653386 and 72.52.
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