The *Pandas DataFrame* has several methods concerning **Computations** and **Descriptive Stats**. When applied to a *DataFrame*, these methods evaluate the elements and return the results.

## Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

- The
*Pandas*library enables access to/from a*DataFrame*. - The
*NumPy*library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt (`$`

), execute the code below. For the terminal used in this example, the command prompt is a dollar sign (`$`

). Your terminal prompt may be different.

$ pip install pandas

Hit the `<Enter>`

key on the keyboard to start the installation process.

$ pip install numpy

Hit the `<Enter>`

key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required libraries.

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd import numpy as np

## DataFrame pct_change()

The `pct_change()`

method calculates and returns the percentage change between the current and prior element(s) in a DataFrame. The return value is the caller.

To fully understand this method and other methods in this tutorial from a mathematical point of view, feel free to watch this short tutorial:

The syntax for this method is as follows:

DataFrame.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)

Parameter | Description |
---|---|

`periods` | This sets the period(s) to calculate the percentage change. |

`fill_method` | This determines what value `NaN` contains. |

`limit` | This sets how many `NaN` values to fill in the DataFrame before stopping. |

`freq` | Used for a specified time series. |

`**kwargs` | Additional keywords are passed into a DataFrame/Series. |

This example calculates and returns the percentage change of four (4) fictitious stocks over three (3) months.

df = pd.DataFrame({'ASL': [18.93, 17.03, 14.87], 'DBL': [39.91, 41.46, 40.99], 'UXL': [44.01, 43.67, 41.98]}, index= ['2021-10-01', '2021-11-01', '2021-12-01']) result = df.pct_change(axis='rows', periods=1) print(result)

- Line [1] creates a
*DataFrame*from a dictionary of lists and saves it to`df`

. - Line [2] uses the
`pc_change()`

method with a selected axis and period to calculate the change. This output saves to the`result`

variable. - Line [3] outputs the result to the terminal.

**Output**

ASL | DBL | UXL | |

2021-10-01 | NaN | NaN | NaN |

2021-11-01 | -0.100370 | 0.038837 | -0.007726 |

2021-12-01 | -0.126835 | -0.011336 | -0.038699 |

💡 **Note**: The first line contains `NaN`

values as there is no previous row.

## DataFrame quantile()

The `quantile()`

method returns the values from a DataFrame/Series at the specified quantile and axis.

The syntax for this method is as follows:

DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

Parameter | Description |
---|---|

`q` | This is a value `0 <= q <= 1` and is the quantile(s) to calculate. |

`axis` | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |

`numeric_only` | Only include columns that contain integers, floats, or boolean values. |

`interpolation` | Calculates the estimated median or quartiles for the DataFrame/Series. |

To fully understand the `interpolation`

parameter from a mathematical point of view, feel free to check out this tutorial:

This example uses the same stock DataFrame as noted above to determine the quantile(s).

df = pd.DataFrame({'ASL': [18.93, 17.03, 14.87], 'DBL': [39.91, 41.46, 40.99], 'UXL': [44.01, 43.67, 41.98]}) result = df.quantile(0.15) print(result)

- Line [1] creates a
*DataFrame*from a dictionary of lists and saves it to`df`

. - Line [2] uses the
`quantile()`

method to calculate by setting the`q`

(quantile) parameter to 0.15. This output saves to the`result`

variable. - Line [3] outputs the result to the terminal.

**Output**

ASL | 15.518 |

DBL | 40.234 |

USL | 42.487 |

Name: 0.15, dtype: float64 |

## DataFrame rank()

The `rank()`

method returns a DataFrame/Series with the values ranked in order. The return value is the same as the caller.

The syntax for this method is as follows:

DataFrame.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)

Parameter | Description |
---|---|

`axis` | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |

`method` | Determines how to rank identical values, such as: – The average rank of the group. – The lowest (min) rank value of the group. – The highest (max) rank value of the group. – Each assigns in the same order they appear in the array. – Density increases by one (1) between the groups. |

`numeric_only` | Only include columns that contain integers, floats, or boolean values. |

`na_option` | Determines how `NaN` values rank, such as: – Keep assigns a NaN to the rank values. – Top: The lowest rank to any NaN values found. – Bottom: The highest to any NaN values found. |

`ascending` | Determines if the elements/values rank in ascending or descending order. |

`pct` | If set to `True` , the results will return in percentile form. By default, this value is `False` . |

For this example, a CSV file is read in and is ranked on Population and sorted. Click here to download and move this file to the current working directory.

df = pd.read_csv("countries.csv") df["Rank"] = df["Population"].rank() df.sort_values("Population", inplace=True) print(df)

- Line [1] reads in the
`countries.csv`

file and saves it to`df`

. - Line [2] appends a column to the end of the DataFrame (
`df`

). - Line [3] sorts the CSV file in ascending order.
- Line [4] outputs the result to the terminal.

**Output**

Country | Capital | Population | Area | Rank | |

4 | Poland | Warsaw | 38383000 | 312685 | 1.0 |

2 | Spain | Madrid | 47431256 | 498511 | 2.0 |

3 | Italy | Rome | 60317116 | 301338 | 3.0 |

1 | France | Paris | 67081000 | 551695 | 4.0 |

0 | Germany | Berlin | 83783942 | 357021 | 5.0 |

5 | Russia | Moscow | 146748590 | 17098246 | 6.0 |

6 | USA | Washington | 328239523 | 9833520 | 7.0 |

8 | India | Dheli | 1352642280 | 3287263 | 8.0 |

7 | China | Beijing | 1400050000 | 9596961 | 9.0 |

## DataFrame round()

The `round()`

method rounds the DataFrame output to a specified number of decimal places.

The syntax for this method is as follows:

DataFrame.round(decimals=0, *args, **kwargs)

Parameter | Description |
---|---|

`decimals` | Determines the specified number of decimal places to round the value(s). |

`*args` | Additional keywords are passed into a DataFrame/Series. |

`**kwargs` | Additional keywords are passed into a DataFrame/Series. |

For this example, the Bank of Canada’s mortgage rates over three (3) months display and round to three (3) decimal places.

**Code Example 1**

df = pd.DataFrame([(2.3455, 1.7487, 2.198)], columns=['Month 1', 'Month 2', 'Month 3']) result = df.round(3) print(result)

- Line [1] creates a
*DataFrame*complete with column names and saves it to`df`

. - Line [2] rounds the mortgage rates to three (3) decimal places. This output saves to the
`result`

variable. - Line [3] outputs the result to the terminal.

**Output**

Month 1 | Month 2 | Month 3 | |

0 | 2.346 | 1.749 | 2.198 |

Another way to perform the same task is with a Lambda!

**Code Example 2**

df = pd.DataFrame([(2.3455, 1.7487, 2.198)], columns=['Month 1', 'Month 2', 'Month 3']) result = df.apply(lambda x: round(x, 3)) print(result)

- Line [1] creates a
*DataFrame*complete with column names and saves it to`df`

. - Line [2] rounds the mortgage rates to three (3) decimal places using a Lambda. This output saves to the
`result`

variable. - Line [3] outputs the result to the terminal.

💡 **Note**: The output is identical to that of the above.

## DataFrame prod() and product()

The `prod()`

and `product()`

methods are identical. Both return the product of the values of a requested axis.

The syntax for these methods is as follows:

DataFrame.prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

DataFrame.product(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Parameter | Description |
---|---|

`axis` | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |

`skipna` | If set to `True` , this parameter excludes NaN/NULL values when calculating the result. |

`level` | Set the appropriate parameter if the DataFrame/Series is multi-level. If no value, then `None` is assumed. |

`numeric_only` | Only include columns that contain integers, floats, or boolean values. |

`min_count` | The number of values on which to perform the calculation. |

`**kwargs` | Additional keywords are passed into a DataFrame/Series. |

For this example, random numbers generate, and the product on the selected axis returns.

df = pd.DataFrame({'A': [2, 4, 6], 'B': [7, 3, 5], 'C': [6, 3, 1]}) index_ = ['A', 'B', 'C'] df.index = index_ result = df.prod(axis=0) print(result)

- Line [1] creates a
*DataFrame*complete with random numbers and saves it to`df`

. - Line [2-3] creates and sets the DataFrame index.
- Line [3] calculates the product along axis 0. This output saves to the
`result`

variable. - Line [4] outputs the result to the terminal.

**Output**

**Formula Example:** 2*4*6=48

A | 48 |

B | 105 |

C | 18 |

dtype: int64 |

## Further Learning Resources

**This is Part 5 of the DataFrame method series.**

Also, have a look at the Pandas DataFrame methods cheat sheet!

At university, I found my love of writing and coding. Both of which I was able to use in my career.

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