Pandas Datetime: Exercises, Practice, Solution
This resource offers a total of 125 Pandas Datetime problems for practice. It includes 25 main exercises, each accompanied by solutions, detailed explanations, and four related problems.
[An Editor is available at the bottom of the page to write and execute the scripts.]
ufo.csv
Extraterrestrials, visitors, little green men, UFOs, swap gas. What do they want? Where do they come from? Do they like cheeseburgers? This dataset will likely not help you answer these questions. It does contain over 80,000 records of UFO sightings dating back as far as 1949. With the latitude and longitude data it is possible to assess the global distribution of UFO sightings (patterns could aid in planetary defense if invasion proves to be imminent). The dates and times, along with the duration of the UFO's stay and description of the craft also lend themselves to predictions. Can we find patterns in their arrival times and durations? Do aliens work on weekends? Help defend the planet and learn about your fellow earthlings (and when they are most likely to see ET).
Data Sources: ufo_sighting_data.csv - [80,332 records]
Exercises data sources: ufo.csv - [347 random records from ufo_sighting_data.csv]
Content
| Column Name | Description |
|---|---|
| Date_time | standardized date and time of sighting |
| city | location of UFO sighting |
| state/province | the US state or Canadian province, appears blank for other locations |
| country | Country of UFO sighting |
| UFO_shape | a one word description of the "spacecraft" |
| length_of_encounter_seconds | standardized to seconds, length of the observation of the UFO |
| described_duration _of_encounter | raw description of the length of the encounter (shows uncertainty to previous column) |
| description | text description of the UFO encounter. Warning column is messy, with some curation it could lend itself to some natural language processing and sentiment analysis. |
| date_documented | when was the UFO sighting reported |
| latitude | latitude |
| longitude | longitude |
Note: There are no missing data in the columns (ufo.csv).
Source:
(a) https://github.com/planetsig/ufo-reports (b) https://www.kaggle.com/camnugent/ufo-sightings-around-the-world
1. Current Date
Write a Pandas program to create the todays date.
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2. UFO Sighting Days from Current Date
Write a Pandas program to calculate all the sighting days of the unidentified flying object (ufo) from current date.
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3. Date Range Analysis for UFO Sightings
Write a Pandas program to get the current date, oldest date and number of days between Current date and oldest date of Ufo dataset.
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4. Recent UFO Sightings (≤ 40 Years)
Write a Pandas program to get all the sighting days of the unidentified flying object (ufo) which are less than or equal to 40 years (365*40 days).
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5. UFO Sightings Between Two Specific Dates
Write a Pandas program to get all the sighting days of the unidentified flying object (ufo) between 1950-10-10 and 1960-10-10.
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6. Extract UFO Sighting Years
Write a Pandas program to get all the sighting years of the unidentified flying object (ufo) and create the year as column.
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7. Plot UFO Reports Per Year
Write a Pandas program to create a plot to present the number of unidentified flying object (UFO) reports per year.
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8. Extract Date Components from UFO Reporting Date
Write a Pandas program to extract year, month, day, hour, minute, second and weekday from unidentified flying object (UFO) reporting date.
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9. Convert Datetime to Timestamp
Write a Pandas program to convert given datetime to timestamp.
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10. Year-Country Wise Frequency of UFO Reports
Write a Pandas program to count year-country wise frequency of reporting dates of unidentified flying object(UFO).
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11. Extract Unique UFO Reporting Dates
Write a Pandas program to extract unique reporting dates of unidentified flying object (UFO).
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12. Calculate Difference Between Documented and Reporting Dates
Write a Pandas program to get the difference (in days) between documented date and reporting date of unidentified flying object (UFO).
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13. Add 100 Days to UFO Reporting Date
Write a Pandas program to add 100 days with reporting date of unidentified flying object (UFO).
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14. Generate Fixed-Frequency Date Sequences
Write a Pandas program to generate sequences of fixed-frequency dates and time spans.
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15. Conversion Between Strings and Datetime
Write a Pandas program to create a conversion between strings and datetime.
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16. Manipulate Datetime with Timezone Information
Write a Pandas program to manipulate and convert date times with timezone information.
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17. Average Mean of UFO Sighting Reports
Write a Pandas program to get the average mean of the UFO (unidentified flying object) sighting was reported.
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18. Graphical Analysis of UFO Sightings by Year
Write a Pandas program to create a graphical analysis of UFO (unidentified flying object) Sightings year.
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19. Check Missing Values in UFO DataFrame
Write a Pandas program to check the empty values of UFO (unidentified flying object) Dataframe.
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20. Plot Distribution of UFO Observation Time
Write a Pandas program to create a plot of distribution of UFO (unidentified flying object) observation time.
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21. Graphical Analysis of UFO Sightings by Month
Write a Pandas program to create a graphical analysis of UFO (unidentified flying object) sighted by month.
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22. Top 10 UFO Years vs. Hours of the Day
Write a Pandas program to create a comparison of the top 10 years in which the UFO was sighted vs the hours of the day.
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23. Top 10 UFO Years vs. Each Month
Write a Pandas program to create a comparison of the top 10 years in which the UFO was sighted vs each Month.
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24. Heatmap for Top 10 UFO Years vs. Each Month
Write a Pandas program to create a heatmap (rectangular data as a color-encoded matrix) for comparison of the top 10 years in which the UFO was sighted vs each Month.
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25. Timewheel: Hour vs. Year Comparison for Top 10 UFO Years
Write a Pandas program to create a Timewheel of Hour Vs Year comparison of the top 10 years in which the UFO was sighted.
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Python Code Editor:
More to Come !
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