Hands-On Exploratory Data Analysis with Python

Data cleaning

Let's now clean our dataset for outliers:

1. We can start by checking the shape of the dataset:

df_power.shape

The output of the preceding code is given here:

(4383, 5)

The dataframe contains 4,283 rows and 5 columns.

2. We can also check few entries inside the dataframe. Let's examine the last 10 entries:

df_power.tail(10)

The output of the preceding code is given here:

3. Next, let's review the data types of each column in our df_power dataframe:

df_power.dtypes

The output of the preceding code is given here:

Date objectConsumption float64Wind float64Solar float64Wind+Solar float64dtype: object

4. Note that the Date column has ...