You can select rows from a DataFrame based on column values by using Boolean indexing or .loc[ ]. These methods will be used to make the data in the library more accessible.
Python pandas library has various methods that will help select rows from the DataFrame in multiple conditions. These operations on the data will assist in analyzing and visualizing it according to the specific dataset provided. Pandas have techniques that can manipulate the DataFrame based on user preference.
Table of Contents:
Selecting Rows from a DataFrame based on Column Values in Python
We can use the following methods to filter rows from a DataFrame based on column values:
- Method 1: Using Boolean Indexing
- To select rows based on a single column
- To select rows based on multiple conditions
- To select rows based on a list of values
- To select rows based on string matching
- Method 2: Using .loc [ ] Method
- Selecting rows based on multiple conditions
- Select rows based on a condition and retrieve specific columns
- Using .loc[] for row and column selection
- Using .loc[] with index labels
- Method 3: Using .query() method
- Method 4: Filtering rows using apply() and lambda
- Method 5: Using None and NaN data
- Filtering rows where a column value is not NaN
- Filtering rows where a column value is not None
Method 1: Using Boolean Indexing to Select Rows in Python
This is the most common method that can be used to select rows from a DataFrame based on column values. It works by combining multiple conditions making the data flexible and allowing users to filter it easily.
1. To select rows based on a single column in Python
For example, you can create a database and based on that you can filter the data as shown below:
Example:
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2. To select rows based on multiple conditions in Python
This is a type of method that filters rows based on more than one condition by using logical operators like & (and), | (or), and ~(not).
For example, to select rows where roll_number is greater than 35 and the Place is “SF”:
Example:
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3. To select rows based on a list of values in Python
You can select the rows where a column value matches the keywords you provide, in that you can use the .isin() method. This method will help you find the data using the keywords provided.
For example, selecting rows where Place is either “LA” or “SF”:
Example:
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4. To select rows based on string matching in Python
This will filter based on string matching using methods like .str.contains() for partial matches. For example, selecting rows where Name includes the substring “a”:
Example:
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Method 2: Using .loc[ ] Method to Select Rows in Python
You can use .loc[ ] to select rows from a pandas DataFrame based on column values by passing a condition inside the .loc[ ] indexer. The condition can be any boolean expression that evaluates to True or False for each row.
1. Selecting rows based on multiple conditions in Python
To select rows based on multiple conditions, you can combine multiple conditions using the & (AND) or | (OR) operators, and wrap each condition in parentheses for proper precedence.
Example:
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2. Select Rows Based on a Condition and Retrieve Specific Column in Python
You can combine row selection and column selection in a single .loc[] statement. For example, if you want to select rows where ‘B’ > 1 and only show column ‘A’.
Example:
Output:
3. Using .loc[ ] for row and column selection in Python
Example:
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4. Using .loc[ ] with index labels in Python
Using .loc[ ] with index labels in pandas allows you to access or filter rows and columns based on the labels of the index, rather than relying on their integer positions.
Example:
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Method 3: Using .query() Method to Select Rows in Python
The .query() method in pandas is a convenient way to filter data from a DataFrame using a string expression. It can be particularly useful for more complex conditions.
You can use logical operators like & (and), | (or), and ~ (not) to combine multiple conditions.
Example: (A > 2 and B == ‘x’)
Output:
Method 4: Filtering Rows Using apply() and lambda Function in Python
We can filter the rows by using the apply() function combined with a lambda function, you can even have conditions for filtering the rows. The apply() function will let you apply those conditions and based on that it will filter either row or column
Example: If you want to filter the rows where the values in column ‘A’ are greater than 2.
Output:
Method 5: Using None and NaN data in Python
We can filter the rows or columns if it has none and Nan values by using the .notna() or .notnull() methods. These methods return a boolean not null value if the values in the DataFrame are not null.
Example:
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1. Filtering rows where a column value is not NaN in Python
To filter rows where a column value is not NaN which indicates that the value is missing, the notna() method will return True for non-NaN values and False for NaN values.
For example, Select rows where column ‘A’ is not NaN.
Example:
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2. Filtering rows where a column value is not None in Python
To filter rows where a column value is not None, The None values can be checked directly using x is not None in the lambda function, you simply check for None.
Example: Select rows where column ‘A’ is not None
Output:
Conclusion
By this, we can conclude that pandas provide various methods to select rows as well as filter the rows by using methods and pre-defined conditions. The techniques that we tailored here are Boolean indexing, .loc[], .query(), and .isin() allow for flexible and efficient filtering based on single or multiple conditions. Besides handling missing values with NaN and None ensure thorough data manipulation for analysis. These methods will enable the user to data usability and effectiveness, making it easier to analyze, and visualize datasets in Python.