Pandas - Analyzing DataFrames
Viewing the Data
One of the most used method for getting a quick overview of the DataFrame, is the head() method.
The head() method returns the headers and
a specified number of rows, starting from the top.
Example
Get a quick overview by printing the first 10 rows of the DataFrame:
df = pd.read_csv('data.csv')
print(df.head(10))
In our examples we will be using a CSV file called 'data.csv'.
Download data.csv, or open data.csv in your browser.
Note: if the number of rows is not specified, the head() method
will return
the top 5 rows.
Example
Print the first 5 rows of the DataFrame:
df = pd.read_csv('data.csv')
print(df.head())
There is also a tail() method for viewing the
last rows of the DataFrame.
The tail() method returns the headers and
a specified number of rows, starting from the bottom.
Info About the Data
The DataFrames object has a method called info(), that
gives you more information about
the data set.
Example
Print information about the data:
Result
<class 'pandas.core.frame.DataFrame'> RangeIndex: 169 entries, 0 to 168 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Duration 169 non-null int64 1 Pulse 169 non-null int64 2 Maxpulse 169 non-null int64 3 Calories 164 non-null float64 dtypes: float64(1), int64(3) memory usage: 5.4 KB None
Result Explained
The result tells us there are 169 rows and 4 columns:
RangeIndex: 169 entries, 0 to 168 Data columns (total 4 columns):
And the name of each column, with the data type:
# Column Non-Null Count Dtype --- ------ -------------- ----- 0 Duration 169 non-null int64 1 Pulse 169 non-null int64 2 Maxpulse 169 non-null int64 3 Calories 164 non-null float64
Null Values
The info() method also tells us how many Non-Null values there are present in each column,
and in our data set it seems like there are 164 of 169 Non-Null values in the "Calories" column.
Which means that there are 5 rows with no value at all, in the "Calories" column, for whatever reason.
Empty values, or Null values, can be bad when analyzing data, and you should consider removing rows with empty values. This is a step towards what is called cleaning data, and you will learn more about that in the next chapters.