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Pandas: Slicing Rows, Conditional Filtering in Python

Updated: at 05:25 AM

Pandas is a popular open-source Python library for data analysis and manipulation. It provides high-performance, easy-to-use data structures and data analysis tools to enable Python developers to work more effectively with structured or tabular data.

One of the key data structures in Pandas is the DataFrame, which can be thought of as a table or spreadsheet of rows and columns. When working with DataFrames in Pandas, it is often necessary to slice or filter rows based on specific conditions in order to focus on relevant subsets of the data.

This comprehensive guide will explain key concepts and provide example code for slicing rows and applying conditional filters on Pandas DataFrames.

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Overview of Pandas DataFrames

A Pandas DataFrame represents tabular data, with rows and columns labeled. It is similar to a SQL table, Excel spreadsheet, or R data frame. DataFrames are mutable and can contain different data types like strings, integers, floats, booleans, etc.

Here is an example DataFrame with player data:

import pandas as pd

data = {'Name': ['John', 'Mary', 'Steve', 'Sarah'],
        'Age': [28, 32, 18, 23],
        'Position': ['Forward', 'Defender', 'Forward', 'Midfielder']}

df = pd.DataFrame(data)
   Name  Age   Position
0  John   28     Forward
1  Mary   32     Defender
2 Steve   18     Forward
3 Sarah   23     Midfielder

The DataFrame has an index (the numbers on the left), column labels, and row labels. We can refer to specific data values by row and column label.

Key properties of DataFrames:

There are many ways to create a Pandas DataFrame:

Now let’s explore how to slice rows and apply filters on DataFrames.

Slicing Rows in Pandas DataFrames

Slicing refers to extracting a subset of rows from a DataFrame based on row positions or labels. It allows focusing analysis on a particular section of the dataset.

There are a few ways to slice DataFrame rows in Python Pandas:

Slicing by Label

We can slice DataFrame rows by label using .loc[].

The label slicing syntax is:

df.loc[start_label : end_label]

This returns rows starting from start_label to end_label.

For example:

# Slice rows by label
  Name  Age    Position
2 Steve   18     Forward
3 Sarah   23     Midfielder

If we omit the end_label, it returns all rows from start_label to the end.

We can also slice a single row by its label, like df.loc['Mary'].

Slicing by Integer Position

We can slice rows by their integer position using .iloc[].

The position slicing syntax is:

df.iloc[start_pos : end_pos]

This returns rows starting from index start_pos to end_pos (exclusive).

For example:

# Slice rows by position
  Name  Age    Position
1 Mary   32     Defender
2 Steve   18     Forward

Again we can omit the end_pos to return all rows to the end. Or use a single integer like df.iloc[2] to get that row.

Slicing a Range of Rows

To select a range of rows, we specify a start and endpoint. Some examples:

# First 5 rows

# All rows between 5 and 10 (exclusive)

# First 10 rows

# All rows from 10 to end

# Every alternate row

We can also use a negative index to count rows from the end:

# Last 3 rows

# All rows except last 3

Stepping in Slices

By providing a step value, we can stride or skip rows while slicing:

# Every 2nd row

# Every 3rd row in reverse

This is useful for subsampling or downsampling rows.

Slicing by Column

We can select single or multiple columns to return alongside the sliced rows:

# Slice two columns with row slice
df.loc['Steve':'Sarah', ['Name', 'Age']]

# Returns only Name column
df.loc['Steve':'Sarah', 'Name']

Overall, slicing by labels .loc provides flexibility while slicing by position .iloc is faster. We can use both techniques based on the situation.

Applying Conditional Filters

A conditional filter allows selecting rows where one or more conditions evaluate to True. This filtering is also referred to as Boolean indexing.

pandas provides methods like .loc[] and .query() to apply such row filters.

Using .loc[] with Boolean Indexing

The .loc[] selector can filter DataFrame rows based on Boolean conditions:


For example:

# Filter for rows where Age column is > 30
df.loc[df['Age'] > 30]
  Name  Age   Position
1 Mary   32     Defender

We can provide multiple filter conditions joined by logical operators:

# Ages > 30 or Position is Defender
df.loc[(df['Age'] > 30) | (df['Position'] == 'Defender')]

Some key logical operators for joining conditions:

We can also filter rows based on their index values:

# Rows with index label 1, 3
df.loc[[1, 3]]

Or by a range of index labels:

# Row index 1 to 3

Using .query() for Filtering

Pandas also provides a .query() method to apply filter conditions:


For example:

# Age > 25 and Position is Defender
df.query('Age > 25 & Position == "Defender"')

We write filter conditions similar to a SQL WHERE clause. The filter string is parsed and used to select matching rows.

Benefits of .query():

Filtering Null or Missing Values

To filter for missing values or nulls, we check for NaN values:

df[df['column'].isnull()]  # rows with NaN in column

df.query('column != column') # null values

The isnull() and notnull() methods are useful for handling missing data.

Filtering by Data Types

We can select rows based on data types of values using the dtype property:

# Numeric columns
df.loc[:, df.dtypes == 'float']

# String columns
df.loc[:, df.dtypes == 'object']

This can filter columns based on types like float, int, string, boolean etc.

Filtering in Pipelines

The query() and filtering operations can be combined with other operations like groupby:

(df.query('Age < 30')
   .agg({'Name': 'count'})

This allows building complex data pipelines.

Resetting Indexes after Slicing

When slicing DataFrame rows, it also slices the index. This can be undesirable in some cases.

To reset the index to a default sequence after slicing, use reset_index():

df.loc[df['Age'] > 30].reset_index(drop=True)

The drop=True option drops the old index entirely instead of adding it as a column.

Avoiding SettingWithCopyWarning

When slicing and filtering DataFrames, we modify a copy rather than original data by default.

pandas raises a warning to prevent accidentally modifying the source.

To avoid this, we can:


Pandas provides versatile facilities for slicing, dicing, and conditioning your data. The .loc, .iloc and .query() methods enable accessing specific DataFrame rows based on labels, positions, and conditions.

Slicing is ideal for reducing data for focused analysis while filtering helps find patterns by narrowing data to relevant samples. Combining the two approaches allows precise, programmatic data analysis.

In summary:

Mastering Pandas slicing and conditional filtering will enable you to handle large, complex datasets with ease. The full power of Python for data analysis is unleashed when you learn how to precisely dissect DataFrames using these tools.