Explore DataFrame information
After you load your data into pandas, the data most likely will be in a DataFrame. However, if the dataset in your DataFrame has 60,000 rows and 400 columns, how do you even begin to get a sense of what you're working with? Fortunately, pandas provides some convenient tools to help you quickly look at overall information about a DataFrame, including the first few and last few rows of data.
To explore DataFrames in pandas, let's import the Python scikit-learn library and use an iconic dataset that every data scientist has seen hundreds of times: British biologist Ronald Fisher's Iris dataset. Fisher used the Iris dataset in his 1936 paper The Use of Multiple Measurements in Taxonomic Problems.
First, import pandas and import the Python scikit-learn dataset:
import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
iris_df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])
DataFrame.info
To take a look at this dataset to see what you have, run:
iris_df.info()
The output looks like this:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 4 columns):
sepal length (cm) 150 non-null float64
sepal width (cm) 150 non-null float64
petal length (cm) 150 non-null float64
petal width (cm) 150 non-null float64
dtypes: float64(4)
memory usage: 4.8 KB
From the output, you know the Iris dataset has 150 entries in four columns. All the data is stored as 64-bit floating-point numbers.
DataFrame.head
Next, let's see what the first few rows of our DataFrame look like. To return the first five rows of data, run:
iris_df.head()
This is what's in the first five rows of the Iris dataset:
| | sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) |
------------------------------------------------------------------------------------
| 0 | 5.1 | 3.5 | 1.4 | 0.2 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
Try it yourself
By default, DataFrame.head returns the first five rows in a DataFrame. In a new code cell, can you figure out how to get it to show more?
Hint (expand to reveal)
Run iris_df.head to consult the documentation.
DataFrame.tail
The flip side of DataFrame.head is DataFrame.tail, which returns the last five rows:
iris_df.tail()
This is what you'll find in the last five rows of the Iris dataset:
| | sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) |
------------------------------------------------------------------------------------
| 145 | 6.7 | 3.0 | 5.2 | 2.3 |
| 147 | 6.3 | 2.5 | 5.0 | 1.9 |
| 148 | 6.5 | 3.0 | 5.2 | 2.0 |
| 149 | 6.2 | 3.4 | 5.4 | 2.3 |
| 150 | 6.9 | 3.0 | 5.1 | 1.8 |
In practice, it's useful to be able to easily examine the first few rows or the last few rows of a DataFrame, particularly when you're looking for outliers in ordered datasets.
Takeaway
Even just by looking at the metadata about the information in a DataFrame, or the first and last few values in one, you can get an immediate idea about the size, shape, and content of the data you're dealing with.