- Pandas - Install Python and Pandas
- Basic Data Structures in Pandas
- Loading and Saving Data using Pandas
- Exploring Data using pandas
- Correlation Analysis using pandas
- Handling Categorical Data and Unique Values using pandas
- Data Visualization using pandas
- Handling Missing Data in Python
- Strategies for Handling Missing Data
- Handling Missing Data - Example - Part 1
- Handling Missing Data - Example - Part 2
- Handling Missing Data - Example - Part 3 (Non-numeric Values)
- Handling Missing Data - Example - Part 4
- Data Transformation and Feature Engineering
- Converting Data Types in Python pandas
- Encoding Categorical Data in Python pandas
- Handling Date and Time Data in Python pandas
- Renaming Columns in Python pandas
- Filtering Rows in a DataFrame in Python
- Merging and Joining Datasets in Python pandas
- Sorting and Indexing Data for Efficient Analysis in Python

# Exploring Data using pandas

When you first load your data, it's important to perform initial checks to understand its structure, content, and the type of data it contains.

### Viewing Data

Here's how you can take a peek at your DataFrame:

```
# Display the first five rows of the stocks DataFrame
print(stocks_df.head())
# Display the last five rows of the sDataFrame
print(stocks_df.tail())
```

**financials_df.head()** displays the first few rows and can immediately flag missing data or anomalies.

**financials_df.tail()** shows you the end of the dataset, often revealing how recent the data is and whether it's been truncated.

### Data Structure

An understanding of your DataFrame's structure is essential before diving into deeper analysis.

We 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.

```
# Print a concise summary of the DataFrame
print(stocks_df.info())
```

### Descriptive Statistics

Descriptive statistics provide a high-level summary of the attributes of your dataset

**stocks_df.describe()**gives a statistical summary for numerical columns, useful for a quick assessment of distribution and variability.- Custom aggregations like
**stocks_df ['****GOOGL '].mean()**help in understanding specific aspects like the average.

```
# Get a statistical summary
print(stocks_df.describe())
# Find the average price for Google
print(stocks_df['GOOGL'].mean())
```

### Aggregation

For more specific summary statistics, you can use aggregation methods like mean(), median(), min(), max(), and sum():

```
# Calculate the average opening price
print(stocks_df['MSFT'].mean())
# Find the maximum closing price
print(stocks_df['MSFT'].max())
```

The result The result will be 61.96290836653386 and 72.52.

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