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"### 23 pandas puzzles \n",
"\n",
"OG repository: https://github.com/ajcr/100-pandas-puzzles (only 60 puzzles there as of Jul 2018)\n",
"\n",
"Inspired by [100 Numpy exerises](https://github.com/rougier/numpy-100), here are short puzzles for testing your knowledge of [pandas'](http://pandas.pydata.org/) power.\n",
"\n",
"Since pandas is a large library with many different specialist features and functions, these excercises focus mainly on the fundamentals of manipulating data (indexing, grouping, aggregating, cleaning), making use of the core DataFrame and Series objects. \n",
"\n",
"Many of the excerises here are stright-forward in that the solutions require no more than a few lines of code (in pandas or NumPy... don't go using pure Python or Cython!). Choosing the right methods and following best practices is the underlying goal.\n",
"\n",
"\n",
"If you're just starting out with pandas and you are looking for some other resources, the official documentation is very extensive. In particular, some good places get a broader overview of pandas are...\n",
"\n",
"- [10 minutes to pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html)\n",
"- [pandas basics](http://pandas.pydata.org/pandas-docs/stable/basics.html)\n",
"- [tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html)\n",
"- [cookbook and idioms](http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook)\n",
"\n",
"Enjoy the puzzles!\n",
"\n",
"\\* *the list of exercises is not yet complete! Pull requests or suggestions for additional exercises, corrections and improvements are welcomed.*"
]
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"## Importing pandas\n",
"\n",
"### Getting started and checking your pandas setup\n",
"\n",
"Difficulty: *easy* \n",
"\n",
"**1.** Import pandas under the name `pd`."
]
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"**2.** Print the version of pandas that has been imported."
]
},
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"**3.** Print out all the version information of the libraries that are required by the pandas library."
]
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"## DataFrame basics\n",
"\n",
"### A few of the fundamental routines for selecting, sorting, adding and aggregating data in DataFrames\n",
"\n",
"Difficulty: *easy*\n",
"\n",
"Note: remember to import numpy using:\n",
"```python\n",
"import numpy as np\n",
"```\n",
"\n",
"Consider the following Python dictionary `data` and Python list `labels`:\n",
"\n",
"``` python\n",
"data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
" 'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
" 'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
" 'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
"\n",
"labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
"```\n",
"(This is just some meaningless data I made up with the theme of animals and trips to a vet.)\n",
"\n",
"**4.** Create a DataFrame `df` from this dictionary `data` which has the index `labels`."
]
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"**5.** Display a summary of the basic information about this DataFrame and its data."
]
},
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"**6.** Return the first 3 rows of the DataFrame `df`."
]
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"**7.** Select just the 'animal' and 'age' columns from the DataFrame `df`."
]
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"**8.** Select the data in rows `[3, 4, 8]` *and* in columns `['animal', 'age']`."
]
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"**9.** Select only the rows where the number of visits is greater than 3."
]
},
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"**10.** Select the rows where the age is missing, i.e. is `NaN`."
]
},
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"**11.** Select the rows where the animal is a cat *and* the age is less than 3."
]
},
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"**12.** Select the rows the age is between 2 and 4 (inclusive)."
]
},
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"**13.** Change the age in row 'f' to 1.5."
]
},
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"**14.** Calculate the sum of all visits (the total number of visits)."
]
},
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"**15.** Calculate the mean age for each different animal in `df`."
]
},
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"**16.** Append a new row 'k' to `df` with your choice of values for each column. Then delete that row to return the original DataFrame."
]
},
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"**17.** Count the number of each type of animal in `df`."
]
},
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"**18.** Sort `df` first by the values in the 'age' in *decending* order, then by the value in the 'visit' column in *ascending* order."
]
},
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"**19.** The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values: 'yes' should be `True` and 'no' should be `False`."
]
},
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"**20.** In the 'animal' column, change the 'snake' entries to 'python'."
]
},
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"**21.** For each animal type and each number of visits, find the mean age. In other words, each row is an animal, each column is a number of visits and the values are the mean ages (hint: use a pivot table)."
]
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"## DataFrames: beyond the basics\n",
"\n",
"### Slightly trickier: you may need to combine two or more methods to get the right answer\n",
"\n",
"Difficulty: *medium*\n",
"\n",
"The previous section was tour through some basic but essential DataFrame operations. Below are some ways that you might need to cut your data, but for which there is no single \"out of the box\" method."
]
},
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"**22.** You have a DataFrame `df` with a column 'A' of integers. For example:\n",
"```python\n",
"df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})\n",
"```\n",
"\n",
"How do you filter out rows which contain the same integer as the row immediately above?"
]
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"**23.** Given a DataFrame of numeric values, say\n",
"```python\n",
"df = pd.DataFrame(np.random.random(size=(5, 3))) # a 5x3 frame of float values\n",
"```\n",
"\n",
"how do you subtract the row mean from each element in the row?"
]
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"source": [
"*More exercises to follow soon...*"
]
}
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