Basic Data Exploration in a Pandas Dataframe

In this exercise today, we will perform some simple data exploration using pandas in python. We will use a dataset that has information about various car models. The data is in a CSV file, mtcars.csv.

The notebook for this tutorial along with the dataset can be found here.

We can start by importing pandas and loading the data into the dataframe.

import pandas as pd
data = pd.read_csv('mtcars.csv')

Now that we have our data in a dataframe, we can take a peak into the data.

data.head()

We can also quickly get some statistics on the data by using the describe function.

data.describe()

We can also get information about the columns and datatypes of each column and the count of non-null values.

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32 entries, 0 to 31
Data columns (total 12 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   model   32 non-null     object 
 1   mpg     32 non-null     float64
 2   cyl     32 non-null     int64  
 3   disp    32 non-null     float64
 4   hp      32 non-null     int64  
 5   drat    32 non-null     float64
 6   wt      32 non-null     float64
 7   qsec    32 non-null     float64
 8   vs      32 non-null     int64  
 9   am      32 non-null     int64  
 10  gear    32 non-null     int64  
 11  carb    32 non-null     int64  
dtypes: float64(5), int64(6), object(1)
memory usage: 3.1+ KB

We can also look for null values in the dataframe.

data.isnull().sum()

model    0
mpg      0
cyl      0
disp     0
hp       0
drat     0
wt       0
qsec     0
vs       0
am       0
gear     0
carb     0
dtype: int64

Now lets say we want to see which model has the maximum MPG. We can do that by finding the row in which the mpg column has the highest values.

data.loc[data['mpg'].idxmax()]

model    Toyota Corolla
mpg                33.9
cyl…