## Pandas Series.rolling()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas

function is a very useful function. It Provides rolling window calculations over the underlying data in the given Series object.** Series.rolling()**

Syntax:Series.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None)

Parameter :

window :Size of the moving window

min_periods :Minimum number of observations in window required to have a value

center :Set the labels at the center of the window.

win_type :Provide a window type.

on :str, optional

axis :int or str, default 0

closed :Make the interval closed on the ‘right’, ‘left’, ‘both’ or ‘neither’ endpoints.

Returns :a Window or Rolling sub-classed for the particular operation

**Example #1:** Use `Series.rolling()`

function to find the rolling window sum of the underlying data for the given Series object. The size of the rolling window should be 2 and the weightage of each element should be same.

`# importing pandas as pd` `import` `pandas as pd` ` ` `# Creating the Series` `sr ` `=` `pd.Series([` `10` `, ` `25` `, ` `3` `, ` `11` `, ` `24` `, ` `6` `])` ` ` `# Create the Index` `index_ ` `=` `[` `'Coca Cola'` `, ` `'Sprite'` `, ` `'Coke'` `, ` `'Fanta'` `, ` `'Dew'` `, ` `'ThumbsUp'` `]` ` ` `# set the index` `sr.index ` `=` `index_` ` ` `# Print the series` `print` `(sr)` |

**Output :**

Now we will use `Series.rolling()`

function to find the sum of the underlying data having a window size of 2.

`# Find sum over a window size of 2` `result ` `=` `sr.rolling(` `2` `).` `sum` `()` ` ` `# Print the returned Series object` `print` `(result)` |

**Output :**

As we can see in the output, the `Series.rolling()`

function has successfully returned a series object having found the sum of the underlying data over a window size of 2. Notice the first value is a missing value as there was no element previous to it so the sum could not be performed.

**Example #2:** Use `Series.rolling()`

function to find the rolling window sum of the underlying data for the given Series object. The size of the rolling window should be 2 and the rolling window type should be ‘triang’.

`# importing pandas as pd` `import` `pandas as pd` ` ` `# Creating the Series` `sr ` `=` `pd.Series([` `10` `, ` `25` `, ` `3` `, ` `11` `, ` `24` `, ` `6` `])` ` ` `# Create the Index` `index_ ` `=` `[` `'Coca Cola'` `, ` `'Sprite'` `, ` `'Coke'` `, ` `'Fanta'` `, ` `'Dew'` `, ` `'ThumbsUp'` `]` ` ` `# set the index` `sr.index ` `=` `index_` ` ` `# Print the series` `print` `(sr)` |

**Output :**

Now we will use `Series.rolling()`

function to find the sum of the underlying data having a window size of 2.

`# Find sum over a window size of 2` `# We have also provided the window type` `result ` `=` `sr.rolling(` `2` `, win_type ` `=` `'triang'` `).` `sum` `()` ` ` `# Print the returned Series object` `print` `(result)` |

**Output :**

As we can see in the output, the `Series.rolling()`

function has successfully returned a series object having found the sum of the underlying data over a window size of 2. Notice the first value is a missing value as there was no element previous to it so the sum could not be performed.

Last Updated on October 23, 2021 by admin