## Calculating Rolling Median in Pandas DataFrame

Pandas is a popular library in Python used for data manipulation and analysis. One of the commonly used functionalities of pandas is calculating rolling statistics, such as the rolling median.

In this article, we will explore how to calculate the rolling median in pandas DataFrame and create a new column to store the rolling median values.

## Example of Calculating Rolling Median

Let’s start with an example. Suppose we have a pandas DataFrame `df`

with one column named ‘values’:

```
import pandas as pd
import numpy as np
np.random.seed(123)
df = pd.DataFrame({'values': np.random.rand(10)})
```

## We can calculate the rolling median for a window of size 3 as follows:

```
rolling_median = df['values'].rolling(window=3).median()
```

The `rolling()`

method returns a rolling window object on which we can apply various rolling statistics, including the median. The `window`

argument specifies the size of the rolling window.

## Types of Rolling Median

There are different types of rolling median, such as the 3-month and 6-month rolling median. To calculate the rolling median for a specific period, we can adjust the window size accordingly.

For example, to calculate the 3-month rolling median, we can set the window size to 3 times the number of rows in 3 months. Assuming our DataFrame has daily data, we can do the following:

```
days_in_month = 30
window_size = days_in_month * 3
rolling_median_3m = df['values'].rolling(window=window_size).median()
```

Similarly, to calculate the 6-month rolling median, we can set the window size to 6 times the number of rows in 6 months.

## Syntax for Calculating Rolling Median

The syntax for calculating the rolling median in pandas DataFrame is quite straightforward. The general syntax is:

```
rolling_median = df['column_name'].rolling(window=window_size).median()
```

where `column_name`

is the name of the column for which we want to compute the rolling median and `window_size`

is the size of the rolling window.

## Creating a New Column with Rolling Median Values

Now let’s say we want to store the rolling median values in a new column of the DataFrame. We can do this by assigning the rolling median values to a new column using the `loc`

accessor.

Here’s how:

```
df.loc[:, 'rolling_median_3m'] = df['values'].rolling(window=window_size).median()
```

This creates a new column in the DataFrame named `rolling_median_3m`

and assigns the 3-month rolling median values to it.

## Manually Verifying Rolling Median Values

It’s always a good practice to double-check the results of our calculations. We can manually verify the rolling median values by using the `rolling()`

method to calculate the median for each window and comparing it with the rolling median values stored in the new column.

Here’s how to do this:

```
verified_rolling_median = []
for i in range(len(df)):
if i < window_size - 1:
verified_rolling_median.append(np.nan)
else:
verified_rolling_median.append(np.median(df.loc[i - window_size + 1:i + 1, 'values']))
```

This manually calculates the rolling median for each window and stores the values in the `verified_rolling_median`

list. We start from the first row with enough data points (i.e., the (window_size-1)th row) and iterate over the entire DataFrame.

## Conclusion

In this article, we have explored how to calculate the rolling median in pandas DataFrame and create a new column to store the rolling median values. We have also seen how to manually verify the rolling median values to ensure their correctness.

With this knowledge, we can now effectively apply rolling statistics to our data and gain more insights from it.

## Additional Pandas Operations

In addition to the rolling median calculation discussed in the previous section, pandas offers several other powerful operations that can help us manipulate and analyze data in various ways. In this section, we will explore some of these additional pandas operations.

### Groupby

One of the most important operations in pandas is `groupby`

. The `groupby`

operation allows us to group data based on one or more columns and apply various aggregations to each group.

For example, suppose we have a DataFrame with columns ‘category’, ‘date’, and ‘value’, representing the category, date, and value of some measurement. We can group the data by category and date and calculate the sum of the values for each group as follows:

```
grouped = df.groupby(['category', 'date']).sum()
```

The `groupby`

method returns a DataFrameGroupBy object, on which we can apply various aggregation functions such as `sum()`

, `mean()`

, `max()`

, `min()`

, `std()`

, `count()`

, etc.

### Pivot Table

Another useful operation in pandas is the `pivot_table`

function. The `pivot_table`

method allows us to transform a DataFrame into a pivot table based on one or more columns.

For example, suppose we have a DataFrame with columns ‘category’, ‘date’, and ‘value’, representing the category, date, and value of some measurement. We can create a pivot table that shows the sum of the values for each category and date combination as follows:

```
pivot = df.pivot_table(values='value', index='category', columns='date', aggfunc='sum')
```

The `values`

argument specifies the column to use for the values in the pivot table.

The `index`

argument specifies the column(s) to use as the row index(es) in the pivot table. The `columns`

argument specifies the column(s) to use as the column index(es) in the pivot table.

The `aggfunc`

argument specifies the aggregation function to apply to the values in each group.

### Merging and Joining

Pandas also allows us to merge and join multiple DataFrames on one or more common columns. The `merge`

function allows us to merge two DataFrames based on one or more common columns.

For example, suppose we have two DataFrames `df1`

and `df2`

with columns ‘key’ and ‘value’, representing some key-value pairs. We can merge the two DataFrames on the ‘key’ column as follows:

```
merged = pd.merge(df1, df2, on='key')
```

The `on`

argument specifies the column(s) to use as the common key(s) for the merge.

We can also merge on multiple columns by specifying a list of column names. The `join`

method is a shorthand function for merging DataFrames on their indexes.

For example, suppose we have two DataFrames `df1`

and `df2`

with the same index and columns ‘value1’ and ‘value2’, representing two different sets of values for each index. We can join the two DataFrames on their index using the `join`

method as follows:

```
joined = df1.join(df2)
```

The `join`

method joins two DataFrames based on their indexes by default, but we can also specify a different common column to join on using the `on`

argument.

### Reshaping

Pandas also provides various operations for reshaping DataFrames, such as `melt`

, `stack`

, and `unstack`

. The `melt`

function allows us to unpivot a DataFrame from wide format to long format.

For example, suppose we have a DataFrame with columns ‘id’, ‘one’, ‘two’, and ‘three’, representing some values for each id. We can melt the DataFrame to long format as follows:

```
melted = pd.melt(df, id_vars=['id'], value_vars=['one', 'two', 'three'], var_name='variable', value_name='value')
```

The `id_vars`

argument specifies the columns to use as the id variables.

The `value_vars`

argument specifies the columns to melt. The `var_name`

argument specifies the name of the new variable column.

The `value_name`

argument specifies the name of the new value column. The `stack`

method allows us to pivot a DataFrame from wide format to long format.

For example, suppose we have a DataFrame with columns ‘id’, ‘one’, ‘two’, and ‘three’, representing some values for each id. We can stack the DataFrame to long format as follows:

```
stacked = df.set_index('id').stack().reset_index().rename(columns={'level_1':'variable', 0:'value'})
```

The `reset_index`

method is used to convert the stacked index back to columns.

The `rename`

method is used to rename the columns to their appropriate names. The `unstack`

method allows us to pivot a DataFrame from long format to wide format.

For example, suppose we have a DataFrame with columns ‘id’, ‘variable’, and ‘value’, representing some values for each id and variable. We can unstack the DataFrame to wide format as follows:

```
unstacked = df.set_index(['id', 'variable']).unstack()
```

The `set_index`

method is used to set the columns ‘id’ and ‘variable’ as the multi-level index for the DataFrame.

The `unstack`

method is used to pivot the DataFrame back to wide format.

## Conclusion

In this section, we have explored some additional pandas operations such as `groupby`

, `pivot_table`

, merging and joining, and reshaping. These operations allow us to manipulate and analyze data in various ways and help us gain more insights from our data.

With these operations in our pandas toolkit, we can effectively handle and analyze data for various data science projects. The article covers various additional operations in pandas, such as `groupby`

, `pivot_table`

, merging and joining, and reshaping.

`groupby`

operation allows us to group data based on one or more columns and apply various aggregations to each group. `pivot_table`

, `merge`

and `join`

functions are used to transform and combine DataFrames based on common keys and indexes.

The reshaping operations include `melt`

, `stack`

and `unstack`

. These operations provide powerful ways to manipulate and analyze data in various ways, thus helping us gain more insights from our data.

With these additional pandas operations, we can effectively handle and analyze data for various data science projects.