Pandas DataFrame mean() method **“returns the mean of the values for the requested axis.” **Applying the mean() method on a Pandas series object returns a scalar value.

**Syntax**

```
DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
```

**Parameters**

**axis:**Axis for the method to be applied.**skipna:**Exclude NA/None values when computing the result.**level:**If the axis is the**MultiIndex**, count along with a specific level, collapsing into the**Series**.**numeric_only:**Include only float, int, and boolean columns. If the values are**None**, I will attempt to use everything, then use only numeric data. However, I have not implemented it for Series.****kwargs:**Additional keyword arguments are to be passed to the function.

**Return Value**

It returns Series or DataFrame (if level specified).

**Use Pandas DataFrame mean() method on Real-time project**

To demonstrate the real-time use of the df.mean() method, we will use the **Kaggle Dataset EtherPriceHistory(USD).**

**Step 1: Load the dataset**

To read the csv file in Pandas DataFrame, use the **pd.read_csv()** method.

```
import pandas as pd
ether_data = pd.read_csv('EtherPriceHistory(USD).csv')
ether_data.head()
```

**Output**

You can see that it provides three columns:

**Date(UTC)**: It is the date of the record.

**UnixTimeStamp:**The Unix timestamp corresponding to the date.

**Value:**The value of Ethereum in USD on that date.

**Step 2: Data Visualization**

You can plot the chart of Ethereum prices over the years based on the dataset. We will plot the Date(UTC) on the x-axis and the Value on the y-axis.

```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
ether_data = pd.read_csv('EtherPriceHistory(USD).csv')
# Set the style for the plot
sns.set_style("whitegrid")
# Plot the trend of Ethereum's value over time
plt.figure(figsize=(14, 7))
sns.lineplot(data=ether_data, x="Date(UTC)", y="Value")
plt.title("Ethereum Value Over Time (USD)")
plt.xlabel("Date")
plt.ylabel("Value (USD)")
plt.show()
```

**Output**

**Step 3: Calculate the mean**

To calculate the mean price of Ethereum, use the **.mean()** method.

```
# Calculate the mean of the 'Value' column
ether_mean_value = ether_data['Value'].mean()
print(ether_mean_value)
```

**Output**

The average value of Ethereum over the provided period in the dataset is approximately USD 211.73**.**

From our analysis, we can say that Ethereum’s value has seen significant fluctuations over time. It started very low, saw rapid growth, and witnessed periods of decline.

You can visualize the mean like this:

This project can serve as a basic introduction to time-series analysis and visualization, and it provides a foundation for more advanced analyses, such as forecasting or understanding factors influencing Ethereum’s price.

I hope you will learn from this real-time project!

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.