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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 100 pandas puzzles
Inspired by 100 Numpy exerises, here are 100* short puzzles for testing your knowledge of pandas' power.
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. 
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.
The exercises are loosely divided in sections. Each section has a difficulty rating; these ratings are subjective, of course, but should be a seen as a rough guide as to how inventive the required solution is.
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...
10 minutes to pandas
pandas basics
tutorials
cookbook and idioms
Enjoy the puzzles!
* the list of exercises is not yet complete! Pull requests or suggestions for additional exercises, corrections and improvements are welcomed.
Importing pandas
Getting started and checking your pandas setup
Difficulty
Step1:  2. Print the version of pandas that has been imported.
Step2:  3. Print out all the version information of the libraries that are required by the pandas library.
Step3:  DataFrame basics
A few of the fundamental routines for selecting, sorting, adding and aggregating data in DataFrames
Difficulty
Step4:  5. Display a summary of the basic information about this DataFrame and its data.
Step5:  6. Return the first 3 rows of the DataFrame df.
Step6:  7. Select just the 'animal' and 'age' columns from the DataFrame df.
Step7:  8. Select the data in rows [3, 4, 8] and in columns ['animal', 'age'].
Step8:  9. Select only the rows where the number of visits is greater than 3.
Step9:  10. Select the rows where the age is missing, i.e. is NaN.
Step10:  11. Select the rows where the animal is a cat and the age is less than 3.
Step11:  12. Select the rows the age is between 2 and 4 (inclusive).
Step12:  13. Change the age in row 'f' to 1.5.
Step13:  14. Calculate the sum of all visits (the total number of visits).
Step14:  15. Calculate the mean age for each different animal in df.
Step15:  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.
Step16:  17. Count the number of each type of animal in df.
Step17:  18. Sort df first by the values in the 'age' in decending order, then by the value in the 'visit' column in ascending order.
Step18:  19. The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values
Step19:  20. In the 'animal' column, change the 'snake' entries to 'python'.
Step20:  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
Step21:  DataFrames
Step22:  23. Given a DataFrame of numeric values, say
python
df = pd.DataFrame(np.random.random(size=(5, 3))) # a 5x3 frame of float values
how do you subtract the row mean from each element in the row?
Step23:  24. Suppose you have DataFrame with 10 columns of real numbers, for example
Step24:  25. How do you count how many unique rows a DataFrame has (i.e. ignore all rows that are duplicates)?
Step25:  The next three puzzles are slightly harder...
26. You have a DataFrame that consists of 10 columns of floating--point numbers. Suppose that exactly 5 entries in each row are NaN values. For each row of the DataFrame, find the column which contains the third NaN value.
(You should return a Series of column labels.)
Step26:  27. A DataFrame has a column of groups 'grps' and and column of numbers 'vals'. For example
Step27:  28. A DataFrame has two integer columns 'A' and 'B'. The values in 'A' are between 1 and 100 (inclusive). For each group of 10 consecutive integers in 'A' (i.e. (0, 10], (10, 20], ...), calculate the sum of the corresponding values in column 'B'.
Step28:  DataFrames
Step29:  Here's an alternative approach based on a cookbook recipe
Step30:  And another approach using a groupby
Step31:  30. Consider a DataFrame containing rows and columns of purely numerical data. Create a list of the row-column index locations of the 3 largest values.
Step32:  31. Given a DataFrame with a column of group IDs, 'grps', and a column of corresponding integer values, 'vals', replace any negative values in 'vals' with the group mean.
Step33:  32. Implement a rolling mean over groups with window size 3, which ignores NaN value. For example consider the following DataFrame
Step34:  Series and DatetimeIndex
Exercises for creating and manipulating Series with datetime data
Difficulty
Step35:  34. Find the sum of the values in s for every Wednesday.
Step36:  35. For each calendar month in s, find the mean of values.
Step37:  36. For each group of four consecutive calendar months in s, find the date on which the highest value occurred.
Step38:  37. Create a DateTimeIndex consisting of the third Thursday in each month for the years 2015 and 2016.
Step39:  Cleaning Data
Making a DataFrame easier to work with
Difficulty
Step40:  39. The From_To column would be better as two separate columns! Split each string on the underscore delimiter _ to give a new temporary DataFrame with the correct values. Assign the correct column names to this temporary DataFrame.
Step41:  40. Notice how the capitalisation of the city names is all mixed up in this temporary DataFrame. Standardise the strings so that only the first letter is uppercase (e.g. "londON" should become "London".)
Step42:  41. Delete the From_To column from df and attach the temporary DataFrame from the previous questions.
Step43:  42. In the Airline column, you can see some extra puctuation and symbols have appeared around the airline names. Pull out just the airline name. E.g. '(British Airways. )' should become 'British Airways'.
Step44:  43. In the RecentDelays column, the values have been entered into the DataFrame as a list. We would like each first value in its own column, each second value in its own column, and so on. If there isn't an Nth value, the value should be NaN.
Expand the Series of lists into a DataFrame named delays, rename the columns delay_1, delay_2, etc. and replace the unwanted RecentDelays column in df with delays.
Step45:  The DataFrame should look much better now.
Using MultiIndexes
Go beyond flat DataFrames with additional index levels
Difficulty
Step46:  45. Check the index of s is lexicographically sorted (this is a necessary proprty for indexing to work correctly with a MultiIndex).
Step47:  46. Select the labels 1, 3 and 6 from the second level of the MultiIndexed Series.
Step48:  47. Slice the Series s; slice up to label 'B' for the first level and from label 5 onwards for the second level.
Step49:  48. Sum the values in s for each label in the first level (you should have Series giving you a total for labels A, B and C).
Step50:  49. Suppose that sum() (and other methods) did not accept a level keyword argument. How else could you perform the equivalent of s.sum(level=1)?
Step51:  50. Exchange the levels of the MultiIndex so we have an index of the form (letters, numbers). Is this new Series properly lexsorted? If not, sort it.
Step52:  Minesweeper
Generate the numbers for safe squares in a Minesweeper grid
Difficulty
Step53:  52. For this DataFrame df, create a new column of zeros (safe) and ones (mine). The probability of a mine occuring at each location should be 0.4.
Step54:  53. Now create a new column for this DataFrame called 'adjacent'. This column should contain the number of mines found on adjacent squares in the grid. 
(E.g. for the first row, which is the entry for the coordinate (0, 0), count how many mines are found on the coordinates (0, 1), (1, 0) and (1, 1).)
Step55:  54. For rows of the DataFrame that contain a mine, set the value in the 'adjacent' column to NaN.
Step56:  55. Finally, convert the DataFrame to grid of the adjacent mine counts
Step57:  Plotting
Visualize trends and patterns in data
Difficulty
Step58:  57. Columns in your DataFrame can also be used to modify colors and sizes.  Bill has been keeping track of his performance at work over time, as well as how good he was feeling that day, and whether he had a cup of coffee in the morning.  Make a plot which incorporates all four features of this DataFrame.
(Hint
Step59:  58.  What if we want to plot multiple things?  Pandas allows you to pass in a matplotlib Axis object for plots, and plots will also return an Axis object.
Make a bar plot of monthly revenue with a line plot of monthly advertising spending (numbers in millions)
df = pd.DataFrame({"revenue"
Step60:  Now we're finally ready to create a candlestick chart, which is a very common tool used to analyze stock price data.  A candlestick chart shows the opening, closing, highest, and lowest price for a stock during a time window.  The color of the "candle" (the thick part of the bar) is green if the stock closed above its opening price, or red if below.
This was initially designed to be a pandas plotting challenge, but it just so happens that this type of plot is just not feasible using pandas' methods.  If you are unfamiliar with matplotlib, we have provided a function that will plot the chart for you so long as you can use pandas to get the data into the correct format.
Your first step should be to get the data in the correct format using pandas' time-series grouping function.  We would like each candle to represent an hour's worth of data.  You can write your own aggregation function which returns the open/high/low/close, but pandas has a built-in which also does this.
The below cell contains helper functions.  Call day_stock_data() to generate a DataFrame containing the prices a hypothetical stock sold for, and the time the sale occurred.  Call plot_candlestick(df) on your properly aggregated and formatted stock data to print the candlestick chart.
Step61:  59. Generate a day's worth of random stock data, and aggregate / reformat it so that it has hourly summaries of the opening, highest, lowest, and closing prices
Step62:  60. Now that you have your properly-formatted data, try to plot it yourself as a candlestick chart.  Use the plot_candlestick(df) function above, or matplotlib's plot documentation if you get stuck.
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Python Code:
import pandas as pd
Explanation: 100 pandas puzzles
Inspired by 100 Numpy exerises, here are 100* short puzzles for testing your knowledge of pandas' power.
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. 
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.
The exercises are loosely divided in sections. Each section has a difficulty rating; these ratings are subjective, of course, but should be a seen as a rough guide as to how inventive the required solution is.
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...
10 minutes to pandas
pandas basics
tutorials
cookbook and idioms
Enjoy the puzzles!
* the list of exercises is not yet complete! Pull requests or suggestions for additional exercises, corrections and improvements are welcomed.
Importing pandas
Getting started and checking your pandas setup
Difficulty: easy 
1. Import pandas under the name pd.
End of explanation
pd.__version__
Explanation: 2. Print the version of pandas that has been imported.
End of explanation
pd.show_versions()
Explanation: 3. Print out all the version information of the libraries that are required by the pandas library.
End of explanation
df = pd.DataFrame(data, index=labels)
Explanation: DataFrame basics
A few of the fundamental routines for selecting, sorting, adding and aggregating data in DataFrames
Difficulty: easy
Note: remember to import numpy using:
python
import numpy as np
Consider the following Python dictionary data and Python list labels:
``` python
data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],
        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],
        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}
labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
```
(This is just some meaningless data I made up with the theme of animals and trips to a vet.)
4. Create a DataFrame df from this dictionary data which has the index labels.
End of explanation
df.info()
# ...or...
df.describe()
Explanation: 5. Display a summary of the basic information about this DataFrame and its data.
End of explanation
df.iloc[:3]
# or equivalently
df.head(3)
Explanation: 6. Return the first 3 rows of the DataFrame df.
End of explanation
df.loc[:, ['animal', 'age']]
# or
df[['animal', 'age']]
Explanation: 7. Select just the 'animal' and 'age' columns from the DataFrame df.
End of explanation
df.loc[df.index[[3, 4, 8]], ['animal', 'age']]
Explanation: 8. Select the data in rows [3, 4, 8] and in columns ['animal', 'age'].
End of explanation
df[df['visits'] > 3]
Explanation: 9. Select only the rows where the number of visits is greater than 3.
End of explanation
df[df['age'].isnull()]
Explanation: 10. Select the rows where the age is missing, i.e. is NaN.
End of explanation
df[(df['animal'] == 'cat') & (df['age'] < 3)]
Explanation: 11. Select the rows where the animal is a cat and the age is less than 3.
End of explanation
df[df['age'].between(2, 4)]
Explanation: 12. Select the rows the age is between 2 and 4 (inclusive).
End of explanation
df.loc['f', 'age'] = 1.5
Explanation: 13. Change the age in row 'f' to 1.5.
End of explanation
df['visits'].sum()
Explanation: 14. Calculate the sum of all visits (the total number of visits).
End of explanation
df.groupby('animal')['age'].mean()
Explanation: 15. Calculate the mean age for each different animal in df.
End of explanation
df.loc['k'] = [5.5, 'dog', 'no', 2]
# and then deleting the new row...
df = df.drop('k')
Explanation: 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.
End of explanation
df['animal'].value_counts()
Explanation: 17. Count the number of each type of animal in df.
End of explanation
df.sort_values(by=['age', 'visits'], ascending=[False, True])
Explanation: 18. Sort df first by the values in the 'age' in decending order, then by the value in the 'visit' column in ascending order.
End of explanation
df['priority'] = df['priority'].map({'yes': True, 'no': False})
Explanation: 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.
End of explanation
df['animal'] = df['animal'].replace('snake', 'python')
Explanation: 20. In the 'animal' column, change the 'snake' entries to 'python'.
End of explanation
df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')
Explanation: 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).
End of explanation
df.loc[df['A'].shift() != df['A']]
Explanation: DataFrames: beyond the basics
Slightly trickier: you may need to combine two or more methods to get the right answer
Difficulty: medium
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.
22. You have a DataFrame df with a column 'A' of integers. For example:
python
df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})
How do you filter out rows which contain the same integer as the row immediately above?
End of explanation
df.sub(df.mean(axis=1), axis=0)
Explanation: 23. Given a DataFrame of numeric values, say
python
df = pd.DataFrame(np.random.random(size=(5, 3))) # a 5x3 frame of float values
how do you subtract the row mean from each element in the row?
End of explanation
df.sum().idxmin()
Explanation: 24. Suppose you have DataFrame with 10 columns of real numbers, for example:
python
df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))
Which column of numbers has the smallest sum? (Find that column's label.)
End of explanation
len(df) - df.duplicated(keep=False).sum()
# or perhaps more simply...
len(df.drop_duplicates(keep=False))
Explanation: 25. How do you count how many unique rows a DataFrame has (i.e. ignore all rows that are duplicates)?
End of explanation
(df.isnull().cumsum(axis=1) == 3).idxmax(axis=1)
Explanation: The next three puzzles are slightly harder...
26. You have a DataFrame that consists of 10 columns of floating--point numbers. Suppose that exactly 5 entries in each row are NaN values. For each row of the DataFrame, find the column which contains the third NaN value.
(You should return a Series of column labels.)
End of explanation
df.groupby('grp')['vals'].nlargest(3).sum(level=0)
Explanation: 27. A DataFrame has a column of groups 'grps' and and column of numbers 'vals'. For example: 
python
df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), 
                   'vals': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})
For each group, find the sum of the three greatest values.
End of explanation
df.groupby(pd.cut(df['A'], np.arange(0, 101, 10)))['B'].sum()
Explanation: 28. A DataFrame has two integer columns 'A' and 'B'. The values in 'A' are between 1 and 100 (inclusive). For each group of 10 consecutive integers in 'A' (i.e. (0, 10], (10, 20], ...), calculate the sum of the corresponding values in column 'B'.
End of explanation
izero = np.r_[-1, (df['X'] == 0).nonzero()[0]] # indices of zeros
idx = np.arange(len(df))
df['Y'] = idx - izero[np.searchsorted(izero - 1, idx) - 1]
# http://stackoverflow.com/questions/30730981/how-to-count-distance-to-the-previous-zero-in-pandas-series/
# credit: Behzad Nouri
Explanation: DataFrames: harder problems
These might require a bit of thinking outside the box...
...but all are solvable using just the usual pandas/NumPy methods (and so avoid using explicit for loops).
Difficulty: hard
29. Consider a DataFrame df where there is an integer column 'X':
python
df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})
For each value, count the difference back to the previous zero (or the start of the Series, whichever is closer). These values should therefore be [1, 2, 0, 1, 2, 3, 4, 0, 1, 2]. Make this a new column 'Y'.
End of explanation
x = (df['X'] != 0).cumsum()
y = x != x.shift()
df['Y'] = y.groupby((y != y.shift()).cumsum()).cumsum()
Explanation: Here's an alternative approach based on a cookbook recipe:
End of explanation
df['Y'] = df.groupby((df['X'] == 0).cumsum()).cumcount()
# We're off by one before we reach the first zero.
first_zero_idx = (df['X'] == 0).idxmax()
df['Y'].iloc[0:first_zero_idx] += 1
Explanation: And another approach using a groupby:
End of explanation
df.unstack().sort_values()[-3:].index.tolist()
# http://stackoverflow.com/questions/14941261/index-and-column-for-the-max-value-in-pandas-dataframe/
# credit: DSM
Explanation: 30. Consider a DataFrame containing rows and columns of purely numerical data. Create a list of the row-column index locations of the 3 largest values.
End of explanation
def replace(group):
    mask = group<0
    group[mask] = group[~mask].mean()
    return group
df.groupby(['grps'])['vals'].transform(replace)
# http://stackoverflow.com/questions/14760757/replacing-values-with-groupby-means/
# credit: unutbu
Explanation: 31. Given a DataFrame with a column of group IDs, 'grps', and a column of corresponding integer values, 'vals', replace any negative values in 'vals' with the group mean.
End of explanation
g1 = df.groupby(['group'])['value']              # group values  
g2 = df.fillna(0).groupby(['group'])['value']    # fillna, then group values
s = g2.rolling(3, min_periods=1).sum() / g1.rolling(3, min_periods=1).count() # compute means
s.reset_index(level=0, drop=True).sort_index()  # drop/sort index
# http://stackoverflow.com/questions/36988123/pandas-groupby-and-rolling-apply-ignoring-nans/
Explanation: 32. Implement a rolling mean over groups with window size 3, which ignores NaN value. For example consider the following DataFrame:
```python
df = pd.DataFrame({'group': list('aabbabbbabab'),
                       'value': [1, 2, 3, np.nan, 2, 3, 
                                 np.nan, 1, 7, 3, np.nan, 8]})
df
   group  value
0      a    1.0
1      a    2.0
2      b    3.0
3      b    NaN
4      a    2.0
5      b    3.0
6      b    NaN
7      b    1.0
8      a    7.0
9      b    3.0
10     a    NaN
11     b    8.0
```
The goal is to compute the Series:
0     1.000000
1     1.500000
2     3.000000
3     3.000000
4     1.666667
5     3.000000
6     3.000000
7     2.000000
8     3.666667
9     2.000000
10    4.500000
11    4.000000
E.g. the first window of size three for group 'b' has values 3.0, NaN and 3.0 and occurs at row index 5. Instead of being NaN the value in the new column at this row index should be 3.0 (just the two non-NaN values are used to compute the mean (3+3)/2)
End of explanation
dti = pd.date_range(start='2015-01-01', end='2015-12-31', freq='B') 
s = pd.Series(np.random.rand(len(dti)), index=dti)
Explanation: Series and DatetimeIndex
Exercises for creating and manipulating Series with datetime data
Difficulty: easy/medium
pandas is fantastic for working with dates and times. These puzzles explore some of this functionality.
33. Create a DatetimeIndex that contains each business day of 2015 and use it to index a Series of random numbers. Let's call this Series s.
End of explanation
s[s.index.weekday == 2].sum() 
Explanation: 34. Find the sum of the values in s for every Wednesday.
End of explanation
s.resample('M').mean()
Explanation: 35. For each calendar month in s, find the mean of values.
End of explanation
s.groupby(pd.TimeGrouper('4M')).idxmax()
Explanation: 36. For each group of four consecutive calendar months in s, find the date on which the highest value occurred.
End of explanation
pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')
Explanation: 37. Create a DateTimeIndex consisting of the third Thursday in each month for the years 2015 and 2016.
End of explanation
df['FlightNumber'] = df['FlightNumber'].interpolate().astype(int)
Explanation: Cleaning Data
Making a DataFrame easier to work with
Difficulty: easy/medium
It happens all the time: someone gives you data containing malformed strings, Python, lists and missing data. How do you tidy it up so you can get on with the analysis?
Take this monstrosity as the DataFrame to use in the following puzzles:
python
df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', 
                               'Budapest_PaRis', 'Brussels_londOn'],
              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],
              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],
                   'Airline': ['KLM(!)', '&lt;Air France&gt; (12)', '(British Airways. )', 
                               '12. Air France', '"Swiss Air"']})
(It's some flight data I made up; it's not meant to be accurate in any way.)
38. Some values in the the FlightNumber column are missing. These numbers are meant to increase by 10 with each row so 10055 and 10075 need to be put in place. Fill in these missing numbers and make the column an integer column (instead of a float column).
End of explanation
temp = df.From_To.str.split('_', expand=True)
temp.columns = ['From', 'To']
Explanation: 39. The From_To column would be better as two separate columns! Split each string on the underscore delimiter _ to give a new temporary DataFrame with the correct values. Assign the correct column names to this temporary DataFrame.
End of explanation
temp['From'] = temp['From'].str.capitalize()
temp['To'] = temp['To'].str.capitalize()
Explanation: 40. Notice how the capitalisation of the city names is all mixed up in this temporary DataFrame. Standardise the strings so that only the first letter is uppercase (e.g. "londON" should become "London".)
End of explanation
df = df.drop('From_To', axis=1)
df = df.join(temp)
Explanation: 41. Delete the From_To column from df and attach the temporary DataFrame from the previous questions.
End of explanation
df['Airline'] = df['Airline'].str.extract('([a-zA-Z\s]+)', expand=False).str.strip()
# note: using .strip() gets rid of any leading/trailing spaces
Explanation: 42. In the Airline column, you can see some extra puctuation and symbols have appeared around the airline names. Pull out just the airline name. E.g. '(British Airways. )' should become 'British Airways'.
End of explanation
# there are several ways to do this, but the following approach is possibly the simplest
delays = df['RecentDelays'].apply(pd.Series)
delays.columns = ['delay_{}'.format(n) for n in range(1, len(delays.columns)+1)]
df = df.drop('RecentDelays', axis=1).join(delays)
Explanation: 43. In the RecentDelays column, the values have been entered into the DataFrame as a list. We would like each first value in its own column, each second value in its own column, and so on. If there isn't an Nth value, the value should be NaN.
Expand the Series of lists into a DataFrame named delays, rename the columns delay_1, delay_2, etc. and replace the unwanted RecentDelays column in df with delays.
End of explanation
letters = ['A', 'B', 'C']
numbers = list(range(10))
mi = pd.MultiIndex.from_product([letters, numbers])
s = pd.Series(np.random.rand(30), index=mi)
Explanation: The DataFrame should look much better now.
Using MultiIndexes
Go beyond flat DataFrames with additional index levels
Difficulty: medium
Previous exercises have seen us analysing data from DataFrames equipped with a single index level. However, pandas also gives you the possibilty of indexing your data using multiple levels. This is very much like adding new dimensions to a Series or a DataFrame. For example, a Series is 1D, but by using a MultiIndex with 2 levels we gain of much the same functionality as a 2D DataFrame.
The set of puzzles below explores how you might use multiple index levels to enhance data analysis.
To warm up, we'll look make a Series with two index levels. 
44. Given the lists letters = ['A', 'B', 'C'] and numbers = list(range(10)), construct a MultiIndex object from the product of the two lists. Use it to index a Series of random numbers. Call this Series s.
End of explanation
s.index.is_lexsorted()
# or more verbosely...
s.index.lexsort_depth == s.index.nlevels
Explanation: 45. Check the index of s is lexicographically sorted (this is a necessary proprty for indexing to work correctly with a MultiIndex).
End of explanation
s.loc[:, [1, 3, 6]]
Explanation: 46. Select the labels 1, 3 and 6 from the second level of the MultiIndexed Series.
End of explanation
s.loc[pd.IndexSlice[:'B', 5:]]
# or equivalently without IndexSlice...
s.loc[slice(None, 'B'), slice(5, None)]
Explanation: 47. Slice the Series s; slice up to label 'B' for the first level and from label 5 onwards for the second level.
End of explanation
s.sum(level=0)
Explanation: 48. Sum the values in s for each label in the first level (you should have Series giving you a total for labels A, B and C).
End of explanation
# One way is to use .unstack()... 
# This method should convince you that s is essentially 
# just a regular DataFrame in disguise!
s.unstack().sum(axis=0)
Explanation: 49. Suppose that sum() (and other methods) did not accept a level keyword argument. How else could you perform the equivalent of s.sum(level=1)?
End of explanation
new_s = s.swaplevel(0, 1)
# check
new_s.index.is_lexsorted()
# sort
new_s = new_s.sort_index()
Explanation: 50. Exchange the levels of the MultiIndex so we have an index of the form (letters, numbers). Is this new Series properly lexsorted? If not, sort it.
End of explanation
p = pd.tools.util.cartesian_product([np.arange(X), np.arange(Y)])
df = pd.DataFrame(np.asarray(p).T, columns=['x', 'y'])
Explanation: Minesweeper
Generate the numbers for safe squares in a Minesweeper grid
Difficulty: medium to hard
If you've ever used an older version of Windows, there's a good chance you've played with [Minesweeper](https://en.wikipedia.org/wiki/Minesweeper_(video_game). If you're not familiar with the game, imagine a grid of squares: some of these squares conceal a mine. If you click on a mine, you lose instantly. If you click on a safe square, you reveal a number telling you how many mines are found in the squares that are immediately adjacent. The aim of the game is to uncover all squares in the grid that do not contain a mine.
In this section, we'll make a DataFrame that contains the necessary data for a game of Minesweeper: coordinates of the squares, whether the square contains a mine and the number of mines found on adjacent squares.
51. Let's suppose we're playing Minesweeper on a 5 by 4 grid, i.e.
X = 5
Y = 4
To begin, generate a DataFrame df with two columns, 'x' and 'y' containing every coordinate for this grid. That is, the DataFrame should start:
x  y
0  0  0
1  0  1
2  0  2
End of explanation
# One way is to draw samples from a binomial distribution.
df['mine'] = np.random.binomial(1, 0.4, X*Y)
Explanation: 52. For this DataFrame df, create a new column of zeros (safe) and ones (mine). The probability of a mine occuring at each location should be 0.4.
End of explanation
# Here is one way to solve using merges.
# It's not necessary the optimal way, just 
# the solution I thought of first...
df['adjacent'] = \
    df.merge(df + [ 1,  1, 0], on=['x', 'y'], how='left')\
      .merge(df + [ 1, -1, 0], on=['x', 'y'], how='left')\
      .merge(df + [-1,  1, 0], on=['x', 'y'], how='left')\
      .merge(df + [-1, -1, 0], on=['x', 'y'], how='left')\
      .merge(df + [ 1,  0, 0], on=['x', 'y'], how='left')\
      .merge(df + [-1,  0, 0], on=['x', 'y'], how='left')\
      .merge(df + [ 0,  1, 0], on=['x', 'y'], how='left')\
      .merge(df + [ 0, -1, 0], on=['x', 'y'], how='left')\
       .iloc[:, 3:]\
        .sum(axis=1)
        
# An alternative solution is to pivot the DataFrame 
# to form the "actual" grid of mines and use convolution.
# See https://github.com/jakevdp/matplotlib_pydata2013/blob/master/examples/minesweeper.py
from scipy.signal import convolve2d
mine_grid = df.pivot_table(columns='x', index='y', values='mine')
counts = convolve2d(mine_grid.astype(complex), np.ones((3, 3)), mode='same').real.astype(int)
df['adjacent'] = (counts - mine_grid).ravel('F')
Explanation: 53. Now create a new column for this DataFrame called 'adjacent'. This column should contain the number of mines found on adjacent squares in the grid. 
(E.g. for the first row, which is the entry for the coordinate (0, 0), count how many mines are found on the coordinates (0, 1), (1, 0) and (1, 1).)
End of explanation
df.loc[df['mine'] == 1, 'adjacent'] = np.nan
Explanation: 54. For rows of the DataFrame that contain a mine, set the value in the 'adjacent' column to NaN.
End of explanation
df.drop('mine', axis=1)\
  .set_index(['y', 'x']).unstack()
Explanation: 55. Finally, convert the DataFrame to grid of the adjacent mine counts: columns are the x coordinate, rows are the y coordinate.
End of explanation
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
df = pd.DataFrame({"xs":[1,5,2,8,1], "ys":[4,2,1,9,6]})
df.plot.scatter("xs", "ys", color = "black", marker = "x")
Explanation: Plotting
Visualize trends and patterns in data
Difficulty: medium
To really get a good understanding of the data contained in your DataFrame, it is often essential to create plots: if you're lucky, trends and anomalies will jump right out at you. This functionality is baked into pandas and the puzzles below explore some of what's possible with the library.
56. Pandas is highly integrated with the plotting library matplotlib, and makes plotting DataFrames very user-friendly! Plotting in a notebook environment usually makes use of the following boilerplate:
python
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
matplotlib is the plotting library which pandas' plotting functionality is built upon, and it is usually aliased to plt.
%matplotlib inline tells the notebook to show plots inline, instead of creating them in a separate window.  
plt.style.use('ggplot') is a style theme that most people find agreeable, based upon the styling of R's ggplot package.
For starters, make a scatter plot of this random data, but use black X's instead of the default markers. 
df = pd.DataFrame({"xs":[1,5,2,8,1], "ys":[4,2,1,9,6]})
Consult the documentation if you get stuck!
End of explanation
df = pd.DataFrame({"productivity":[5,2,3,1,4,5,6,7,8,3,4,8,9],
                   "hours_in"    :[1,9,6,5,3,9,2,9,1,7,4,2,2],
                   "happiness"   :[2,1,3,2,3,1,2,3,1,2,2,1,3],
                   "caffienated" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})
df.plot.scatter("hours_in", "productivity", s = df.happiness * 30, c = df.caffienated)
Explanation: 57. Columns in your DataFrame can also be used to modify colors and sizes.  Bill has been keeping track of his performance at work over time, as well as how good he was feeling that day, and whether he had a cup of coffee in the morning.  Make a plot which incorporates all four features of this DataFrame.
(Hint:  If you're having trouble seeing the plot, try multiplying the Series which you choose to represent size by 10 or more)
The chart doesn't have to be pretty: this isn't a course in data viz!
df = pd.DataFrame({"productivity":[5,2,3,1,4,5,6,7,8,3,4,8,9],
                   "hours_in"    :[1,9,6,5,3,9,2,9,1,7,4,2,2],
                   "happiness"   :[2,1,3,2,3,1,2,3,1,2,2,1,3],
                   "caffienated" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})
End of explanation
df = pd.DataFrame({"revenue":[57,68,63,71,72,90,80,62,59,51,47,52],
                   "advertising":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],
                   "month":range(12)
                  })
ax = df.plot.bar("month", "revenue", color = "green")
df.plot.line("month", "advertising", secondary_y = True, ax = ax)
ax.set_xlim((-1,12))
Explanation: 58.  What if we want to plot multiple things?  Pandas allows you to pass in a matplotlib Axis object for plots, and plots will also return an Axis object.
Make a bar plot of monthly revenue with a line plot of monthly advertising spending (numbers in millions)
df = pd.DataFrame({"revenue":[57,68,63,71,72,90,80,62,59,51,47,52],
                   "advertising":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],
                   "month":range(12)
                  })
End of explanation
#This function is designed to create semi-interesting random stock price data
import numpy as np
def float_to_time(x):
    return str(int(x)) + ":" + str(int(x%1 * 60)).zfill(2) + ":" + str(int(x*60 % 1 * 60)).zfill(2)
def day_stock_data():
    #NYSE is open from 9:30 to 4:00
    time = 9.5
    price = 100
    results = [(float_to_time(time), price)]
    while time < 16:
        elapsed = np.random.exponential(.001)
        time += elapsed
        if time > 16:
            break
        price_diff = np.random.uniform(.999, 1.001)
        price *= price_diff
        results.append((float_to_time(time), price))
    
    
    df = pd.DataFrame(results, columns = ['time','price'])
    df.time = pd.to_datetime(df.time)
    return df
def plot_candlestick(agg):
    fig, ax = plt.subplots()
    for time in agg.index:
        ax.plot([time.hour] * 2, agg.loc[time, ["high","low"]].values, color = "black")
        ax.plot([time.hour] * 2, agg.loc[time, ["open","close"]].values, color = agg.loc[time, "color"], linewidth = 10)
    ax.set_xlim((8,16))
    ax.set_ylabel("Price")
    ax.set_xlabel("Hour")
    ax.set_title("OHLC of Stock Value During Trading Day")
    plt.show()
Explanation: Now we're finally ready to create a candlestick chart, which is a very common tool used to analyze stock price data.  A candlestick chart shows the opening, closing, highest, and lowest price for a stock during a time window.  The color of the "candle" (the thick part of the bar) is green if the stock closed above its opening price, or red if below.
This was initially designed to be a pandas plotting challenge, but it just so happens that this type of plot is just not feasible using pandas' methods.  If you are unfamiliar with matplotlib, we have provided a function that will plot the chart for you so long as you can use pandas to get the data into the correct format.
Your first step should be to get the data in the correct format using pandas' time-series grouping function.  We would like each candle to represent an hour's worth of data.  You can write your own aggregation function which returns the open/high/low/close, but pandas has a built-in which also does this.
The below cell contains helper functions.  Call day_stock_data() to generate a DataFrame containing the prices a hypothetical stock sold for, and the time the sale occurred.  Call plot_candlestick(df) on your properly aggregated and formatted stock data to print the candlestick chart.
End of explanation
df = day_stock_data()
df.head()
df.set_index("time", inplace = True)
agg = df.resample("H").ohlc()
agg.columns = agg.columns.droplevel()
agg["color"] = (agg.close > agg.open).map({True:"green",False:"red"})
agg.head()
Explanation: 59. Generate a day's worth of random stock data, and aggregate / reformat it so that it has hourly summaries of the opening, highest, lowest, and closing prices
End of explanation
plot_candlestick(agg)
Explanation: 60. Now that you have your properly-formatted data, try to plot it yourself as a candlestick chart.  Use the plot_candlestick(df) function above, or matplotlib's plot documentation if you get stuck.
End of explanation
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