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6 Must-Know Pandas Techniques for Data Manipulation in Python



INTRODUCTION


Data manipulation is the process of reorganizing data to make it easier to read and presentable. Real-world data is messy. That’s why libraries like pandas are so valuable. Pandas is an open-source library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.


Using pandas you can take the pain out of data manipulation by extracting, filtering, and transforming data in DataFrames, clearing a path for quick and reliable data analysis.


In this blog, we will discover some of the most important data manipulation techniques using pandas. Techniques will be discussed are:

  1. Query and Filtering

  2. Grouping and Aggregation

  3. Pivot Table

  4. Apply Method

  5. Counting

  6. Dropping Duplicates


LET'S Begin!


1. Query and Filtering

We can query the DataFrame object as we send queries to an SQL database table. Thee query method takes a string parameter which is similar to the condition that we pass to the WHERE clause in SQL.


For example:

Considering the common iris dataset, and we want to select the data points where the sepal length is more than 7 cm. Calling the head method on the DataFrame will show the first 5 rows in a tabular format.

And now we call the query method passing our condition:

We can get the same result by manually filtering the DataFrame object:

myfilter = df['sepal length (cm)'] > 7
df[myfilter]

This will result in the same results:



2. Grouping and Aggregation

2.1 Aggregation

Aggregation in pandas provides various functions that perform a mathematical or logical operation on our dataset and returns a summary of that function.

  • Aggregation can be used to get a summary of columns in our dataset like getting sum, minimum, maximum, etc. from a particular column of our dataset.

  • The function used for aggregation is agg(), the parameter is the function we want to perform.

  • Some functions used in the aggregation are:

For example:

Let's first create a sample dataset:

df = pd.DataFrame([[9, 4, 8, 9],
                   [8, 10, 7, 6],
                   [7, 6, 8, 5]],
                  columns=['Maths',  'English', 
                           'Science', 'History'])
  
print(df)

By applying the sum() function we calculate the sum of every value.



We can also aggregate by multiple functions:

2.2 Grouping

Grouping is used to group data using some criteria from our dataset. It is used as split-apply-combine strategy in the following manner:

  1. Splitting the data into groups based on some criteria.

  2. Applying a function to each group independently.

  3. Combining the results into a data structure.

Considering the previous dataset which contains students marks in various subjects:

Maths

English

Science

History

0

9

4

8

9

1

8

10

7

6

2

7

6

8

5

Let's use groupby() function to group the data on “Maths” value. This will return a DataFrameGroupBy object as a result.

We can inspect the content of the DataFrameGroupBy object by calling first on it:


As we can see, the function created three groups because there are three unique values in the Maths column; 7, 8, 9. Each group contains the corresponding values for the rest of the columns; English, Science, and History.


Let's see a real life example, using grouping and aggregation.


Consider the following dataset that contains records about daily observations (4 days) of temperatures, windspeeds, events in various cities.


and we want to know What is the maximum temperature in each city during the first 4 days in Jan 2017.

To do so, we will perform the following steps:

  1. Grouping the DataFrame by city

  2. Selecting only the temperature column of each group

  3. Aggregate each group using the max function

max_temps = df.groupby('city')['temperature'].agg('max')
max_temps

3. Pivot Table

Using the function pivot we can quickly create a different view of the data and summarize large amount of data by automatically grouping aggregating data.


Considering the same weather dataset. Instead of raw view that shows duplicating days and cities. We want a table that shows values of corresponding to each day and city without the duplicates shown in the original view.


We can do this by calling the pivot function on the DataFrame object, passing the column day as the table's index, the column city as the table' columns and selecting only the temperature column of the resulting pivot table.

df.pivot(index='day', columns='city')['temperature']

4. Apply Method

The apply method is used for automatically applying a specific function on a DataFrame column(s).


For example:

Let's first create a sample dataset contains student's first name, last name, and average.

And suppose that we don't want to show averages that below or equal 50, and replace the average with the text "Fail".


First, we implement a function that takes a number and return "Fail" when the number is less than or equal 50.

def grading(num):
    if num <= 50:
        return "Fail"
    else:
        return num

And finally we pass that function to the apply method:

df['Average'] = df['Average'].apply(grading)

5. Counting

Using the value_counts() method on a specific DataFrame column will result in counting how many times a unique value has been showed up in the data. For example, counting on the column event in the weather dataset:

df['event'].value_counts()

6. Dropping Duplicates

Considering the following sample dataset:

As we can see the rows: 0, and 1 are duplicates. Calling drop_duplicates() function will keep only one of the observations.


Moreover, we can pass a subset parameter to look for duplicates in specific columns instead of all of them.


That's it!

Thanks for reading!

Note: You can find code included in the blog in my Github.


Best Regards.


Acknowledgment


References