Intro to Pandas for Data Analysis

In this project, you'll practice Vectorized Operations using data from Oceanographic readings from wether stations in the south of Argentina, including temperature, humidity of the air, minerals in the water, and much more!

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

input

Enter the value with up to 1 decimal. If the found value is 40.123, enter only `40.1`

.

input

Enter the value with up to 1 decimal. If the found value is 40.123, enter only `40.1`

.

input

Enter the value with up to 1 decimal. If the found value is 40.123, enter only `40.1`

.

codevalidated

Use the series `temp_max`

and `temp_min`

to create a new series `temp_range`

that contains the difference in temperature between the Max and the Min for each day.

codevalidated

The humidity range is important for understanding the variability in moisture levels throughout the day. It helps in assessing how much the humidity fluctuates, which can impact comfort and weather patterns.

It's calculated using the formula:

```
Humidity Range = Humidity_Max - Humidity_Min
```

Where:

`Humidity_Max`

is the maximum humidity of the day as a percentage.`Humidity_Min`

is the minimum humidity of the day as a percentage.

This formula provides a simple measure of the daily range in humidity levels.

Create the new series `humidity_range`

by combining the series `humidity_max`

and `humidity_min`

.

codevalidated

The wind speed range is important for understanding the variability in wind conditions throughout the day. It helps in assessing how much the wind speed fluctuates, which can impact weather conditions and perceived temperature.

It's calculated using the formula:

```
Wind Speed Range = Wind_Speed_Max - Wind_Speed_Min
```

Where:

`Wind_Speed_Max`

is the maximum wind speed of the day in kilometers per hour.`Wind_Speed_Min`

is the minimum wind speed of the day in kilometers per hour.

This formula provides a simple measure of the daily range in wind speeds.

Create the new series `wind_speed_range`

by combining the series `wind_speed_max`

and `wind_speed_min`

.

codevalidated

Normalization is a common preprocessing step in data science. Normalize the `chlorophyll`

series by subtracting the mean of the series and dividing by the standard deviation. The resulting series should be `chlorophyll_normalized`

.

The formula to normalize it is:

```
chlorophyll_normalized = (chlorophyll - AVG(chlorophyll)) / STD DEV(chlorophyll)
```

codevalidated

The "density_anomaly" is an important concept in oceanography for understanding how variations in seawater density affect ocean circulation and stratification.

Typically, density is calculated using a complex formula involving temperature, salinity, and pressure. For this exercise, we will approximate the density anomaly using a simplified formula:

```
density_anomaly = 1000 - (0.2 * sea_temperature) + (0.8 * salinity)
```

This formula subtracts a weighted combination of `sea_temperature`

and `salinity`

from a baseline density value of 1000 kg/m³. This approximation helps in grasping the basic concept of how temperature and salinity influence seawater density.

codevalidated

The wind chill index is important for understanding the perceived temperature on the human body under cold and windy conditions. It takes into account the effect of wind speed in making the air feel colder than the actual temperature.

It's calculated using the formula:

```
Wind Chill = 13.12 + 0.6215 * Temp_Min - 11.37 * (Wind_Speed_Max ^ 0.16) + 0.3965 * Temp_Min * (Wind_Speed_Max ^ 0.16)
```

Where:

`Temp_Min`

is the minimum temperature of the day in degrees Celsius.`Wind_Speed_Max`

is the maximum wind speed of the day in kilometers per hour.

Create the new series `wind_chill_index`

by combining the series `temp_min`

and `wind_speed_max`

.

codevalidated

The ocean nutrient index is important for assessing the availability of key nutrients that support phytoplankton growth, which forms the base of the marine food web.

It's calculated using the formula:

```
Ocean Nutrient Index = (Phosphate + Silicate + Nitrito+Nitrato) / 3
```

Where:

`Phosphate`

is the concentration of phosphate in µM.`Silicate`

is the concentration of silicate in µM.`Nitrito+Nitrato`

is the combined concentration of nitrite and nitrate in µM.

This index provides a simplified measure of the overall nutrient availability in the ocean.

Create the new series `ocean_nutrient_index`

by combining the series `phosphate`

, `silicate`

, and `nitrite_nitrate`

.

codevalidated

The humidity comfort index is important for understanding how the combination of temperature and humidity affects human comfort. High humidity combined with high temperatures can make conditions feel much hotter and more uncomfortable.

It's calculated using the formula:

```
Humidity Comfort Index = Temp_Avg - (0.55 - 0.0055 * Humidity_Avg) * (Temp_Avg - 14.5)
```

Where:

`Temp_Avg`

is the average temperature in degrees Celsius.`Humidity_Avg`

is the average humidity as a percentage.

This index gives an adjusted temperature value that reflects how the temperature feels when accounting for humidity.

Create the new series `humidity_comfort_index`

by combining the series `humidity_avg`

and `temp_avg`

.

codevalidated

The storm potential index is important for evaluating the likelihood of storm conditions by combining key meteorological factors. High wind speeds, humidity, and temperature are often associated with stormy weather.

It's calculated using the formula:

```
Storm Potential Index = (Wind_Speed_Max * Humidity_Max * Temp_Max) / 1000
```

Where:

`Wind_Speed_Max`

is the maximum wind speed of the day in kilometers per hour.`Humidity_Max`

is the maximum humidity of the day as a percentage.`Temp_Max`

is the maximum temperature of the day in degrees Celsius.

This formula provides a scaled index that reflects the potential for stormy conditions by considering the interaction of these factors.

Create the new series `storm_potential_index`

by combining the series `wind_speed_max`

, `humidity_max`

, and `temp_max`

.

codevalidated

The `comfort_temp_diff_ratio`

is important for understanding how fluctuations in daily temperature impact perceived human comfort, as indicated by the humidity comfort index.

It's calculated using the formula:

```
Comfort Temp Diff Ratio = Humidity_Comfort_Index / temp_range
```

Where:

`Humidity_Comfort_Index`

reflects perceived comfort based on average temperature and humidity.`temp_range`

is the difference between the maximum and minimum temperature for each day.

This ratio helps assess how daily temperature changes influence overall comfort.

Create the new series `comfort_temp_diff_ratio`

by combining the series `humidity_comfort_index`

and `temp_range`

.

codevalidated

The `storm_wind_correlation`

is important for exploring how variability in wind speed might amplify or dampen the potential for storm conditions.

It's calculated using the formula:

```
Storm Wind Correlation = Storm_Potential_Index * Wind_Speed_Range
```

Where:

`Storm_Potential_Index`

is a scaled index reflecting the potential for storm conditions based on maximum wind speed, humidity, and temperature.`Wind_Speed_Range`

is the difference between the maximum and minimum wind speeds for each day.

This product helps assess the relationship between wind variability and storm potential.

Create the new series `storm_wind_correlation`

by combining the series `storm_potential_index`

and `wind_speed_range`

.

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