Ella Walter
Pandas is a Python package that makes importing and analyzing data much easier. It allows you to select specific columns from a DataFrame using various methods such as column labels, index positions, and ranges. You can use the .
| Characteristics | Values |
|---|---|
| Selecting specific columns | Use square brackets with the column name of interest. |
| Selecting multiple columns | Use a list of column names within the selection brackets. |
| Selecting rows | Use the row's index (number on the far left). |
| Selecting specific rows and columns | Use the loc/iloc operators in front of the selection brackets. |
| Excluding columns | Drop them in the column index. |
| Displaying all columns and rows | Go to the options configuration in Pandas and use "display.max_columns". |
| Changing the number of rows | Change the "max_rows" option. |
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What You'll Learn
Using loc[] to select columns by name
The loc[] function in Pandas is used to select rows and columns from a DataFrame using the row and column labels. It is particularly useful when you know the labels of the rows and columns you want to select.
To use loc[], you need to specify the row labels and column labels inside the square brackets. For example, if you want to select the rows with labels "Orange" and "Yellow" and the columns with labels "id" and "person" from a DataFrame called "df", you would use the following code:
Python
Df.loc[["Orange", "Yellow"], ["id", "person"]]
This will return a new DataFrame containing only the specified rows and columns.
Another example would be selecting the rows from 3 to 7, along with columns "volatile_acidity" to "chlorides". The code for this would be:
Python
Df.loc[2:6, "volatile_acidity":"chlorides"]
Note that when using loc[], you can use a single label, a list of labels, or a slice of labels. You can also use a colon to specify that you want to select all rows or columns. For example, to select all rows and only the "id" and "person" columns, you can use the following code:
Python
Df.loc[:, ["id", "person"]]
This will return a new DataFrame with all rows and only the specified columns.
It's important to note that loc[] is different from iloc[], which is based on the integer position of the rows and columns. loc[] is particularly useful when you have meaningful labels for your rows and columns and want to select data based on those labels.
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Using iloc[] to select columns by index position
The .
To use .iloc[], you need to provide integer indices for both the rows and columns you want to select. For example, df.iloc [1, 2] selects the value at row 1 and column 2. You can also use slicing to select a range of rows or columns, such as df.iloc [1:3, 0:3], which selects rows 1 and 2 and columns 0, 1, and 2.
Additionally, .iloc[] supports various operations like selecting single or multiple rows/columns or specific subsets. It also allows for boolean indexing, where you can select rows or columns based on specific conditions. For example, you can use df.iloc [list(df ['Fee'] >= 24000)] to select rows where the 'Fee' column is greater than or equal to 24000.
It's important to note that .iloc[] is exclusive of the end position in slices, similar to standard Python slicing. It will also raise an IndexError if a requested indexer is out of bounds, except for slice indexers which allow out-of-bounds indexing.
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Selecting columns by range
Using loc[]
The loc method is one of the most widely used techniques for selecting data in Pandas. It allows you to select data from a dataframe based on row labels and column labels. To select columns by range using loc, you can specify the range of columns within the square brackets. For example:
Python
Df[df.columns[1:4]]
This code will select all rows and the second to fourth columns. You can also use loc to select specific rows within a specified range of columns:
Python
Df.loc[0:10, 'a':'b']
This code will select rows with indices 0 to 10 and columns from 'a' to 'b'.
Using iloc[]
The iloc method is used to subset dataframes based on the position or index of the rows and columns. It is useful when you want to select data numerically without knowing the column names. Here's an example of selecting columns by range using iloc:
Python
Df.iloc[:, 0:2]
This code will select all rows and the first two columns. Note that Python does not include the ending index in the slice.
Using take()
The take() function can be used to select columns by index. It creates a copy of the dataframe by default, avoiding the SettingWithCopyWarning. Here's an example:
Python
Df.take([0, 2], axis=1)
This code will select the first and third columns of the dataframe.
Using xs()
The xs() function is used to select columns by label and can be particularly useful for MultiIndex columns. Here's an example:
Python
Df.xs(pd.Index(['A', 'B']), axis=1)
This code will select columns 'A' and 'B' from the dataframe.
Using boolean indexing
Boolean indexing involves creating a boolean mask that indicates which values in the dataframe meet certain criteria. This mask is then used to select the desired values. Here's an example:
Python
Df[df['Age'] > 25]
This code will select rows where the value in the 'Age' column is greater than 25.
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Selecting columns by slice
When selecting specific columns in a pandas dataframe, you can use the .loc and
The .loc method is used to select rows and columns by their
Python
Df = pd.DataFrame(np.random.rand(10, 5), columns = list('abcde'))
Df_new = df[['a', 'd']]
This will create a new dataframe, `df_new`, that contains only the 'a' and 'd' columns from the original dataframe, `df`.
The .iloc method is used to select rows and columns by their integer index. Using the same column labels as before, if you want to select columns 'a' and 'd', you would use the following code:
Python
Df = pd.DataFrame(np.random.rand(10, 5), columns = list('abcde'))
Df_new = df.iloc[:, [0, 3]]
This will create a new dataframe, `df_new`, that contains only the first and fourth columns (columns 'a' and 'd') from the original dataframe, `df`.
You can also use slicing to select multiple columns. For example, to select columns 'a' through 'c', you can use the following code:
Python
Df_new = df.iloc[:, 0:3]
This will create a new dataframe, `df_new`, that contains only the first three columns (columns 'a', 'b', and 'c') from the original dataframe, `df`.
It's important to note that when using .iloc, the ending index is not included in the slice. So, in the example above, `df.iloc[:, 0:3]` will select columns with indices 0, 1, and 2, but not column 3.
Additionally, you can use slicing to select columns within a specific range. For example, to select columns 'a' through 'c' by every second column, you can use the following code:
Python
Df_new = df.loc[:, 'a':'c':2]
This will create a new dataframe, `df_new`, that contains columns 'a' and 'c' from the original dataframe, `df`.
You can also use slicing to select columns from the beginning or end of the dataframe. For example, to select columns from the beginning to 'b', you can use the following code:
Python
Df_new = df.loc[:, :'b']
This will create a new dataframe, `df_new`, that contains columns from the beginning up to and including column 'b' from the original dataframe, `df`.
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Selecting columns using square brackets
To select specific columns in pandas, you can use square brackets [] in combination with the column names or their indices. Here's a step-by-step guide on how to use square brackets for column selection:
Selecting a Single Column
To select a single column from a DataFrame, use square brackets [] with the column name of interest. For example, if you have a DataFrame called "titanic" and want to select the "Age" column, you would use the following syntax:
Python
Titanic ["Age"]
The returned object from selecting a single column is a pandas Series, which is 1-dimensional and only contains the data from that column.
Selecting Multiple Columns
To select multiple columns, you can use a list of column names within the square brackets []. For example, if you want to select both the "Age" and "Sex" columns from the "titanic" DataFrame, your code would look like this:
Python
Titanic [["Age", "Sex"]]
By providing a list of column names, you will get a pandas DataFrame as the output, which is 2-dimensional and contains the selected columns.
Using loc with Square Brackets
You can also use the "loc" operator in conjunction with square brackets for more advanced column selection. The "loc" operator allows you to specify both rows and columns to extract specific data. For example, if you want to select the "Age" and "Sex" columns for the first three passengers in the "titanic" DataFrame, you would use:
Python
Titanic.loc [0:2, ["Age", "Sex"]]
The "loc" operator provides more flexibility in selecting subsets of data by combining row and column indices or labels.
Filtering Rows with Conditional Expressions
Square brackets can also be used to filter rows based on conditional expressions. For instance, if you want to find passengers older than 35 years old, you can use the following code:
Python
Above_35 = titanic [titanic ["Age"] > 35]
This code creates a new DataFrame, "above_35," containing only the rows where the "Age" column value is greater than 35.
Using isin() Conditional Function
The "isin () function is useful for filtering rows based on specific values. For example, to find passengers from cabin classes 2 and 3, you can use:
Python
Class_23 = titanic [titanic ["Pclass"].isin ([2, 3])]
This code selects rows where the "Pclass" column contains either 2 or 3, assigning them to the "class_23" DataFrame.
In summary, square brackets are a fundamental tool in pandas for selecting columns, filtering rows, and creating subsets of data. By mastering the techniques outlined above, you can efficiently manipulate and analyze DataFrames to extract valuable insights from your data.
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