Selecting, Extracting and Slicing Dataframes Pandas

Selecting Dataframes

Selecting involves extracting specific rows, or columns from a Pandas dataframe. Let’s look at the various methods available in Pandas to select data from dataframes.

Column Selection

You can select one or more columns from a dataframe using their names. You can either use dot notation (df.column_name) or bracket notation (df['column_name']) to access columns. Let’s look at an example.

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col_name = df['Name'] # or df.Name
print(col_name)

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Output:

We selected an entire column from the Titanic dataframe in this example using the column name and bracket notation.

Row Selection

You can select a specific row using the row index. In the following example, we will use the .iloc[] indexer for performing the required integer-based indexing.

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selected_row = df.iloc[0]
print(selected_row)

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Output:

Boolean Indexing

You can use boolean conditions to filter rows based on specific criteria. Let us take a look at an example to understand this better.

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filtered = df[df['Name']=='Kelly, Mr. James']
print(filtered)

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Output:

Here we filtered the rows based on the Name column value.

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filtered = df[df['Age']<10]
filtered.head()

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Output:

In this example, the filtering was done based on the Age column value.

The following section will teach you about extracting dataframes.

Extracting Dataframes

Extracting involves getting values present at some particular index in our dataframe.

There are two ways of extracting information from dataframes, integer-based and label-based.

Integer-based Extraction

You can use integer-based indexing to select specific values by their integer positions within the dataframe using the .iat[] indexer, which uses the following syntax:-

dataframe.iat[row_index, col_index]

Let’s take a look at an example.

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Python

extracted_value = df.iat[0, 2]
print(extracted_value)

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Output:

Here we extracted the value present at the (0, 2) index from our dataframe table.

Label-based Extraction

You can use label-based indexing to select values by their labels using .at[] indexer, which uses the following syntax:-

dataframe.at[row_label, col_label]

Row indices serve as row labels, and column names serve as column labels.

Now, let’s take a look at an example.

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Python

extracted_value = df.at[0, "Fare"]
print(extracted_value)

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Output:

Here we extracted the value using the row label 0 and column label Fare.

In the next section, you will learn about slicing dataframes in Pandas.

Slicing Dataframes

Slicing is a type of dataframe selection where a subset of contiguous rows and columns are selected from a dataframe. It allows you to extract specific portions of data for further manipulation.

There are two ways of slicing dataframes, integer-based and label-based slicing.

Integer-based Slicing

You can use integer-based indexing to select rows and columns by their integer positions within the dataframe using the .iloc[] indexer, which uses the following syntax:-

dataframe.iloc[row_start:row_end, col_start:col_end]

Let’s take a look at some examples.

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sliced_df = df.iloc[0:3, 0:2]  # Rows 0 to 2 and Columns 0 to 1
print(sliced_df)

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Output:

Here we selected rows [0, 2] and columns [0, 1]. The next example will cover selecting contiguous columns using the .iloc[] indexer.

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sliced_df = df.iloc[:, 0:2]  # Columns 0 to 1
print(sliced_df)

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Output:

As we only specified the column indices, the result contains those columns and all the rows.

Label-based Slicing

You can use label-based indexing to select rows and columns by their labels using .loc[] indexer, which uses the following syntax:-

dataframe.loc[row_label_start:row_label_end, col_label_start:col_label_end]

Now, let’s take a look at an example.

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sliced_df = df.loc[5:10, "Name":"Age"]
print(sliced_df)

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Output:

Here we selected the columns from Name to Age and rows 5 to 10.

Frequently Asked Questions

What is the difference between a Dataframe and a Series in Pandas?

Both Dataframe and Series are types of data structures provided by the Pandas library.  A Dataframe is a two-dimensional data structure that resembles a table, while a Series is a one-dimensional array-like structure.

What parameters can be used with the read_csv() function?

Some commonly used parameters include delimiter, header, usecols, skiprows, nrows, and na_values. These parameters allow you to customize how the CSV file is read and loaded.

What is the difference between selecting and slicing in Pandas?

Selecting involves choosing specific columns and rows from a dataframe, and Slicing refers to extracting contiguous subsets of rows and/or columns from a Dataframe or Series. Both are ways of accessing specific data from a dataframe.

Conclusion

In this article, you learned how to install Pandas and the process of selecting, extracting, and slicing information from dataframes with the help of examples.

Read the following articles to learn more about Pandas:-

• Getting Started with Pandas
   
• Pandas Pivot Table
   
• Advanced functions in Pandas

Happy Learning!