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Python Pandas IMDb Movies Data: Exercises, Practice, Solution

[An editor is available at the bottom of the page to write and execute the scripts.]

Sample Table (based on IMDb - movies csv):
Download movies_metadata.csv table (50) records from here.

Pandas IMDb Movies Data Analysis [17 exercises with solution]

1. Write a Python Pandas program to get the columns of the DataFrame (movies_metadata.csv file). Go to the editor
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2. Write a Pandas program to get the information of the DataFrame (movies_metadata.csv file)including data types and memory usage. Go to the editor
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3. Write a Pandas program to get the details of the third movie of the DataFrame (movies_metadata.csv file). Go to the editor
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4. Write a Pandas program to count the number of rows and columns of the DataFrame (movies_metadata.csv file). Go to the editor
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5. Write a Pandas program to get the details of the columns title and genres of the DataFrame. Go to the editor
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6. Write a Pandas program to get the details of the movie with title 'Grumpier Old Men'. Go to the editor
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7. Write a Pandas program to get the details of  fifth movie of the DataFrame. Go to the editor
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8. Write a Pandas program to create a smaller DataFrame with a subset of all features. Go to the editor
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9. Write a Pandas program to display the first 10 rows of the DataFrame. Go to the editor
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10. Write a Pandas program to sort the DataFrame based on release_date. Go to the editor
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11. Write a Pandas program to access those movies, released after 1995-01-01.Go to the editor
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12. Write a Pandas program to sort movies on runtime in descending order. Go to the editor
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13. Write a Pandas program to get those movies whose revenue more than 2 million and spent less than 1 million. Go to the editor
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14. Write a Pandas program to get the longest runtime and shortest runtime. Go to the editor
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15. Write a Pandas program to calculate the number of votes garnered by the 70% movie. Go to the editor
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16. Write a Pandas program to display the movies (title, runtime) longer than 30 minutes and shorter than 360 minutes. Go to the editor
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17. Write a Pandas program to display the movies (title, number of votes) that received specified number of votes. Go to the editor
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Python-Pandas Code Editor:


More to Come !

Do not submit any solution of the above exercises at here, if you want to contribute go to the appropriate exercise page.

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Python: Tips of the Day

Understanding slice notation:

It's pretty simple really:

a[start:stop]  # items start through stop-1
a[start:]      # items start through the rest of the array
a[:stop]       # items from the beginning through stop-1
a[:]           # a copy of the whole array

There is also the step value, which can be used with any of the above:

a[start:stop:step] # start through not past stop, by step

The key point to remember is that the :stop value represents the first value that is not in the selected slice. So, the difference between stop and start is the number of elements selected (if step is 1, the default).

The other feature is that start or stop may be a negative number, which means it counts from the end of the array instead of the beginning. So:

a[-1]    # last item in the array
a[-2:]   # last two items in the array
a[:-2]   # everything except the last two items

Similarly, step may be a negative number:

a[::-1]    # all items in the array, reversed
a[1::-1]   # the first two items, reversed
a[:-3:-1]  # the last two items, reversed
a[-3::-1]  # everything except the last two items, reversed

Python is kind to the programmer if there are fewer items than you ask for. For example, if you ask for a[:-2] and a only contains one element, you get an empty list instead of an error. Sometimes you would prefer the error, so you have to be aware that this may happen.

Relation to slice() object

The slicing operator [] is actually being used in the above code with a slice() object using the : notation (which is only valid within []), i.e.:

a[start:stop:step]

is equivalent to:

a[slice(start, stop, step)]

Slice objects also behave slightly differently depending on the number of arguments, similarly to range(), i.e. both slice(stop) and slice(start, stop[, step]) are supported. To skip specifying a given argument, one might use None, so that e.g. a[start:] is equivalent to a[slice(start, None)] or a[::-1] is equivalent to a[slice(None, None, -1)].

While the : -based notation is very helpful for simple slicing, the explicit use of slice() objects simplifies the programmatic generation of slicing.

Ref: https://bit.ly/2MHaTp7