from __future__ import division, print_function, unicode_literals

"""
Explanation: Tools - pandas
The pandas library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the DataFrame, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available programmatically, such as creating pivot tables, computing columns based on other columns, plotting graphs, etc. You can also group rows by column value, or join tables much like in SQL. Pandas is also great at handling time series.
Prerequisites:
* NumPy – if you are not familiar with NumPy, we recommend that you go through the NumPy tutorial now.
Setup
First, let's make sure this notebook works well in both python 2 and 3:
End of explanation
"""


import pandas as pd

"""
Explanation: Now let's import pandas. People usually import it as pd:
End of explanation
"""


s = pd.Series([2,-1,3,5])
s

"""
Explanation: Series objects
The pandas library contains these useful data structures:
* Series objects, that we will discuss now. A Series object is 1D array, similar to a column in a spreadsheet (with a column name and row labels).
* DataFrame objects. This is a 2D table, similar to a spreadsheet (with column names and row labels).
* Panel objects. You can see a Panel as a dictionary of DataFrames. These are less used, so we will not discuss them here.
Creating a Series
Let's start by creating our first Series object!
End of explanation
"""


import numpy as np
np.exp(s)

"""
Explanation: Similar to a 1D ndarray
Series objects behave much like one-dimensional NumPy ndarrays, and you can often pass them as parameters to NumPy functions:
End of explanation
"""


s + [1000,2000,3000,4000]

"""
Explanation: Arithmetic operations on Series are also possible, and they apply elementwise, just like for ndarrays:
End of explanation
"""


s + 1000

"""
Explanation: Similar to NumPy, if you add a single number to a Series, that number is added to all items in the Series. This is called * broadcasting*:
End of explanation
"""


s < 0

"""
Explanation: The same is true for all binary operations such as * or /, and even conditional operations:
End of explanation
"""


s2 = pd.Series([68, 83, 112, 68], index=["alice", "bob", "charles", "darwin"])
s2

"""
Explanation: Index labels
Each item in a Series object has a unique identifier called the index label. By default, it is simply the rank of the item in the Series (starting at 0) but you can also set the index labels manually:
End of explanation
"""


s2["bob"]

"""
Explanation: You can then use the Series just like a dict:
End of explanation
"""


s2[1]

"""
Explanation: You can still access the items by integer location, like in a regular array:
End of explanation
"""


s2.loc["bob"]

s2.iloc[1]

"""
Explanation: To make it clear when you are accessing by label or by integer location, it is recommended to always use the loc attribute when accessing by label, and the iloc attribute when accessing by integer location:
End of explanation
"""


s2.iloc[1:3]

"""
Explanation: Slicing a Series also slices the index labels:
End of explanation
"""


surprise = pd.Series([1000, 1001, 1002, 1003])
surprise

surprise_slice = surprise[2:]
surprise_slice

"""
Explanation: This can lead to unexpected results when using the default numeric labels, so be careful:
End of explanation
"""


try:
    surprise_slice[0]
except KeyError as e:
    print("Key error:", e)

"""
Explanation: Oh look! The first element has index label 2. The element with index label 0 is absent from the slice:
End of explanation
"""


surprise_slice.iloc[0]

"""
Explanation: But remember that you can access elements by integer location using the iloc attribute. This illustrates another reason why it's always better to use loc and iloc to access Series objects:
End of explanation
"""


weights = {"alice": 68, "bob": 83, "colin": 86, "darwin": 68}
s3 = pd.Series(weights)
s3

"""
Explanation: Init from dict
You can create a Series object from a dict. The keys will be used as index labels:
End of explanation
"""


s4 = pd.Series(weights, index = ["colin", "alice"])
s4

"""
Explanation: You can control which elements you want to include in the Series and in what order by explicitly specifying the desired index:
End of explanation
"""


print(s2.keys())
print(s3.keys())

s2 + s3

"""
Explanation: Automatic alignment
When an operation involves multiple Series objects, pandas automatically aligns items by matching index labels.
End of explanation
"""


s5 = pd.Series([1000,1000,1000,1000])
print("s2 =", s2.values)
print("s5 =", s5.values)

s2 + s5

"""
Explanation: The resulting Series contains the union of index labels from s2 and s3. Since "colin" is missing from s2 and "charles" is missing from s3, these items have a NaN result value. (ie. Not-a-Number means missing).
Automatic alignment is very handy when working with data that may come from various sources with varying structure and missing items. But if you forget to set the right index labels, you can have surprising results:
End of explanation
"""


meaning = pd.Series(42, ["life", "universe", "everything"])
meaning

"""
Explanation: Pandas could not align the Series, since their labels do not match at all, hence the full NaN result.
Init with a scalar
You can also initialize a Series object using a scalar and a list of index labels: all items will be set to the scalar.
End of explanation
"""


s6 = pd.Series([83, 68], index=["bob", "alice"], name="weights")
s6

"""
Explanation: Series name
A Series can have a name:
End of explanation
"""


%matplotlib inline
import matplotlib.pyplot as plt
temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]
s7 = pd.Series(temperatures, name="Temperature")
s7.plot()
plt.show()

"""
Explanation: Plotting a Series
Pandas makes it easy to plot Series data using matplotlib (for more details on matplotlib, check out the matplotlib tutorial). Just import matplotlib and call the plot method:
End of explanation
"""


dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')
dates

"""
Explanation: There are many options for plotting your data. It is not necessary to list them all here: if you need a particular type of plot (histograms, pie charts, etc.), just look for it in the excellent Visualization section of pandas' documentation, and look at the example code.
Handling time
Many datasets have timestamps, and pandas is awesome at manipulating such data:
* it can represent periods (such as 2016Q3) and frequencies (such as "monthly"),
* it can convert periods to actual timestamps, and vice versa,
* it can resample data and aggregate values any way you like,
* it can handle timezones.
Time range
Let's start by creating a time series using timerange. This returns a DatetimeIndex containing one datetime per hour for 12 hours starting on October 29th 2016 at 5:30pm.
End of explanation
"""


temp_series = pd.Series(temperatures, dates)
temp_series

"""
Explanation: This DatetimeIndex may be used as an index in a Series:
End of explanation
"""


temp_series.plot(kind="bar")

plt.grid(True)
plt.show()

"""
Explanation: Let's plot this series:
End of explanation
"""


temp_series_freq_2H = temp_series.resample("2H")
temp_series_freq_2H

"""
Explanation: Resampling
Pandas let's us resample a time series very simply. Just call the resample method and specify a new frequency:
End of explanation
"""


temp_series_freq_2H.plot(kind="bar")
plt.show()

"""
Explanation: Let's take a look at the result:
End of explanation
"""


temp_series_freq_2H = temp_series.resample("2H", how=np.min)
temp_series_freq_2H

"""
Explanation: Note how the values have automatically been aggregated into 2-hour periods. If we look at the 6-8pm period, for example, we had a value of 5.1 at 6:30pm, and 6.1 at 7:30pm. After resampling, we just have one value of 5.6, which is the mean of 5.1 and 6.1. Computing the mean is the default behavior, but it is also possible to use a different aggregation function, for example we can decide to keep the minimum value of each period:
End of explanation
"""


temp_series_freq_15min = temp_series.resample("15Min")
temp_series_freq_15min.head(n=10) # `head` displays the top n values

"""
Explanation: Upsampling and interpolation
This was an example of downsampling. We can also upsample (ie. increase the frequency), but this creates holes in our data:
End of explanation
"""


temp_series_freq_15min = temp_series.resample("15Min").interpolate(method="cubic")
temp_series_freq_15min.head(n=10)

temp_series.plot(label="Period: 1 hour")
temp_series_freq_15min.plot(label="Period: 15 minutes")
plt.legend()
plt.show()

"""
Explanation: One solution is to fill the gaps by interpolating. We just call the interpolate method. The default is to use linear interpolation, but we can also select another method, such as cubic interpolation:
End of explanation
"""


temp_series_ny = temp_series.tz_localize("America/New_York")
temp_series_ny

"""
Explanation: Timezones
By default datetimes are naive: they are not aware of timezones, so 2016-10-30 02:30 might mean October 30th 2016 at 2:30am in Paris or in New York. We can make datetimes timezone aware by calling the tz_localize method:
End of explanation
"""


temp_series_paris = temp_series_ny.tz_convert("Europe/Paris")
temp_series_paris

"""
Explanation: Note that -04:00 is now appended to all the datetimes. This means that these datetimes refer to UTC - 4 hours.
We can convert these datetimes to Paris time like this:
End of explanation
"""


temp_series_paris_naive = temp_series_paris.tz_localize(None)
temp_series_paris_naive

"""
Explanation: You may have noticed that the UTC offset changes from +02:00 to +01:00: this is because France switches to winter time at 3am that particular night (time goes back to 2am). Notice that 2:30am occurs twice! Let's go back to a naive representation (if you log some data hourly using local time, without storing the timezone, you might get something like this):
End of explanation
"""


try:
    temp_series_paris_naive.tz_localize("Europe/Paris")
except Exception as e:
    print(type(e))
    print(e)

"""
Explanation: Now 02:30 is really ambiguous. If we try to localize these naive datetimes to the Paris timezone, we get an error:
End of explanation
"""


temp_series_paris_naive.tz_localize("Europe/Paris", ambiguous="infer")

"""
Explanation: Fortunately using the ambiguous argument we can tell pandas to infer the right DST (Daylight Saving Time) based on the order of the ambiguous timestamps:
End of explanation
"""


quarters = pd.period_range('2016Q1', periods=8, freq='Q')
quarters

"""
Explanation: Periods
The period_range function returns a PeriodIndex instead of a DatetimeIndex. For example, let's get all quarters in 2016 and 2017:
End of explanation
"""


quarters + 3

"""
Explanation: Adding a number N to a PeriodIndex shifts the periods by N times the PeriodIndex's frequency:
End of explanation
"""


quarters.asfreq("M")

"""
Explanation: The asfreq method lets us change the frequency of the PeriodIndex. All periods are lengthened or shortened accordingly. For example, let's convert all the quarterly periods to monthly periods (zooming in):
End of explanation
"""


quarters.asfreq("M", how="start")

"""
Explanation: By default, the asfreq zooms on the end of each period. We can tell it to zoom on the start of each period instead:
End of explanation
"""


quarters.asfreq("A")

"""
Explanation: And we can zoom out:
End of explanation
"""


quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index = quarters)
quarterly_revenue

quarterly_revenue.plot(kind="line")
plt.show()

"""
Explanation: Of course we can create a Series with a PeriodIndex:
End of explanation
"""


last_hours = quarterly_revenue.to_timestamp(how="end", freq="H")
last_hours

"""
Explanation: We can convert periods to timestamps by calling to_timestamp. By default this will give us the first day of each period, but by setting how and freq, we can get the last hour of each period:
End of explanation
"""


last_hours.to_period()

"""
Explanation: And back to periods by calling to_period:
End of explanation
"""


months_2016 = pd.period_range("2016", periods=12, freq="M")
one_day_after_last_days = months_2016.asfreq("D") + 1
last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()
last_bdays.to_period("H") + 9

"""
Explanation: Pandas also provides many other time-related functions that we recommend you check out in the documentation. To whet your appetite, here is one way to get the last business day of each month in 2016, at 9am:
End of explanation
"""


people_dict = {
    "weight": pd.Series([68, 83, 112], index=["alice", "bob", "charles"]),
    "birthyear": pd.Series([1984, 1985, 1992], index=["bob", "alice", "charles"], name="year"),
    "children": pd.Series([0, 3], index=["charles", "bob"]),
    "hobby": pd.Series(["Biking", "Dancing"], index=["alice", "bob"]),
}
people = pd.DataFrame(people_dict)
people

"""
Explanation: DataFrame objects
A DataFrame object represents a spreadsheet, with cell values, column names and row index labels. You can define expressions to compute columns based on other columns, create pivot-tables, group rows, draw graphs, etc. You can see DataFrames as dictionaries of Series.
Creating a DataFrame
You can create a DataFrame by passing a dictionary of Series objects:
End of explanation
"""


people["birthyear"]

"""
Explanation: A few things to note:
* the Series were automatically aligned based on their index,
* missing values are represented as NaN,
* Series names are ignored (the name "year" was dropped),
* DataFrames are displayed nicely in Jupyter notebooks, woohoo!
You can access columns pretty much as you would expect. They are returned as Series objects:
End of explanation
"""


people[["birthyear", "hobby"]]

"""
Explanation: You can also get multiple columns at once:
End of explanation
"""


d2 = pd.DataFrame(
        people_dict,
        columns=["birthyear", "weight", "height"],
        index=["bob", "alice", "eugene"]
     )
d2

"""
Explanation: If you pass a list of columns and/or index row labels to the DataFrame constructor, it will guarantee that these columns and/or rows will exist, in that order, and no other column/row will exist. For example:
End of explanation
"""


values = [
            [1985, np.nan, "Biking",   68],
            [1984, 3,      "Dancing",  83],
            [1992, 0,      np.nan,    112]
         ]
d3 = pd.DataFrame(
        values,
        columns=["birthyear", "children", "hobby", "weight"],
        index=["alice", "bob", "charles"]
     )
d3

"""
Explanation: Another convenient way to create a DataFrame is to pass all the values to the constructor as an ndarray, or a list of lists, and specify the column names and row index labels separately:
End of explanation
"""


masked_array = np.ma.asarray(values, dtype=np.object)
masked_array[(0, 2), (1, 2)] = np.ma.masked
d3 = pd.DataFrame(
        values,
        columns=["birthyear", "children", "hobby", "weight"],
        index=["alice", "bob", "charles"]
     )
d3

"""
Explanation: To specify missing values, you can either use np.nan or NumPy's masked arrays:
End of explanation
"""


d4 = pd.DataFrame(
         d3,
         columns=["hobby", "children"],
         index=["alice", "bob"]
     )
d4

"""
Explanation: Instead of an ndarray, you can also pass a DataFrame object:
End of explanation
"""


people = pd.DataFrame({
    "birthyear": {"alice":1985, "bob": 1984, "charles": 1992},
    "hobby": {"alice":"Biking", "bob": "Dancing"},
    "weight": {"alice":68, "bob": 83, "charles": 112},
    "children": {"bob": 3, "charles": 0}
})
people

"""
Explanation: It is also possible to create a DataFrame with a dictionary (or list) of dictionaries (or list):
End of explanation
"""


d5 = pd.DataFrame(
  {
    ("public", "birthyear"):
        {("Paris","alice"):1985, ("Paris","bob"): 1984, ("London","charles"): 1992},
    ("public", "hobby"):
        {("Paris","alice"):"Biking", ("Paris","bob"): "Dancing"},
    ("private", "weight"):
        {("Paris","alice"):68, ("Paris","bob"): 83, ("London","charles"): 112},
    ("private", "children"):
        {("Paris", "alice"):np.nan, ("Paris","bob"): 3, ("London","charles"): 0}
  }
)
d5

"""
Explanation: Multi-indexing
If all columns are tuples of the same size, then they are understood as a multi-index. The same goes for row index labels. For example:
End of explanation
"""


d5["public"]

d5["public", "hobby"]  # Same result as d4["public"]["hobby"]

"""
Explanation: You can now get a DataFrame containing all the "public" columns very simply:
End of explanation
"""


d5

"""
Explanation: Dropping a level
Let's look at d5 again:
End of explanation
"""


d5.columns = d5.columns.droplevel(level = 0)
d5

"""
Explanation: There are two levels of columns, and two levels of indices. We can drop a column level by calling droplevel (the same goes for indices):
End of explanation
"""


d6 = d5.T
d6

"""
Explanation: Transposing
You can swap columns and indices using the T attribute:
End of explanation
"""


d7 = d6.stack()
d7

"""
Explanation: Stacking and unstacking levels
Calling the stack method will push the lowest column level after the lowest index:
End of explanation
"""


d8 = d7.unstack()
d8

"""
Explanation: Note that many NaN values appeared. This makes sense because many new combinations did not exist before (eg. there was no bob in London).
Calling unstack will do the reverse, once again creating many NaN values.
End of explanation
"""


d9 = d8.unstack()
d9

"""
Explanation: If we call unstack again, we end up with a Series object:
End of explanation
"""


d10 = d9.unstack(level = (0,1))
d10

"""
Explanation: The stack and unstack methods let you select the level to stack/unstack. You can even stack/unstack multiple levels at once:
End of explanation
"""


people

"""
Explanation: Most methods return modified copies
As you may have noticed, the stack and unstack methods do not modify the object they apply to. Instead, they work on a copy and return that copy. This is true of most methods in pandas.
Accessing rows
Let's go back to the people DataFrame:
End of explanation
"""


people.loc["charles"]

"""
Explanation: The loc attribute lets you access rows instead of columns. The result is Series object in which the DataFrame's column names are mapped to row index labels:
End of explanation
"""


people.iloc[2]

"""
Explanation: You can also access rows by integer location using the iloc attribute:
End of explanation
"""


people.iloc[1:3]

"""
Explanation: You can also get a slice of rows, and this returns a DataFrame object:
End of explanation
"""


people[np.array([True, False, True])]

"""
Explanation: Finally, you can pass a boolean array to get the matching rows:
End of explanation
"""


people[people["birthyear"] < 1990]

"""
Explanation: This is most useful when combined with boolean expressions:
End of explanation
"""


people

people["age"] = 2016 - people["birthyear"]  # adds a new column "age"
people["over 30"] = people["age"] > 30      # adds another column "over 30"
birthyears = people.pop("birthyear")
del people["children"]

people

birthyears

"""
Explanation: Adding and removing columns
You can generally treat DataFrame objects like dictionaries of Series, so the following work fine:
End of explanation
"""


people["pets"] = pd.Series({"bob": 0, "charles": 5, "eugene":1})  # alice is missing, eugene is ignored
people

"""
Explanation: When you add a new colum, it must have the same number of rows. Missing rows are filled with NaN, and extra rows are ignored:
End of explanation
"""


people.insert(1, "height", [172, 181, 185])
people

"""
Explanation: When adding a new column, it is added at the end (on the right) by default. You can also insert a column anywhere else using the insert method:
End of explanation
"""


people.assign(
    body_mass_index = people["weight"] / (people["height"] / 100) ** 2,
    has_pets = people["pets"] > 0
)

"""
Explanation: Assigning new columns
You can also create new columns by calling the assign method. Note that this returns a new DataFrame object, the original is not modified:
End of explanation
"""


try:
    people.assign(
        body_mass_index = people["weight"] / (people["height"] / 100) ** 2,
        overweight = people["body_mass_index"] > 25
    )
except KeyError as e:
    print("Key error:", e)

"""
Explanation: Note that you cannot access columns created within the same assignment:
End of explanation
"""


d6 = people.assign(body_mass_index = people["weight"] / (people["height"] / 100) ** 2)
d6.assign(overweight = d6["body_mass_index"] > 25)

"""
Explanation: The solution is to split this assignment in two consecutive assignments:
End of explanation
"""


try:
    (people
         .assign(body_mass_index = people["weight"] / (people["height"] / 100) ** 2)
         .assign(overweight = people["body_mass_index"] > 25)
    )
except KeyError as e:
    print("Key error:", e)

"""
Explanation: Having to create a temporary variable d6 is not very convenient. You may want to just chain the assigment calls, but it does not work because the people object is not actually modified by the first assignment:
End of explanation
"""


(people
     .assign(body_mass_index = lambda df: df["weight"] / (df["height"] / 100) ** 2)
     .assign(overweight = lambda df: df["body_mass_index"] > 25)
)

"""
Explanation: But fear not, there is a simple solution. You can pass a function to the assign method (typically a lambda function), and this function will be called with the DataFrame as a parameter:
End of explanation
"""


people.eval("weight / (height/100) ** 2 > 25")

"""
Explanation: Problem solved!
Evaluating an expression
A great feature supported by pandas is expression evaluation. This relies on the numexpr library which must be installed.
End of explanation
"""


people.eval("body_mass_index = weight / (height/100) ** 2")
people

"""
Explanation: Assignment expressions are also supported, and contrary to the assign method, this does not create a copy of the DataFrame, instead it directly modifies it:
End of explanation
"""


overweight_threshold = 30
people.eval("overweight = body_mass_index > @overweight_threshold")
people

"""
Explanation: You can use a local or global variable in an expression by prefixing it with '@':
End of explanation
"""


people.query("age > 30 and pets == 0")

"""
Explanation: Querying a DataFrame
The query method lets you filter a DataFrame based on a query expression:
End of explanation
"""


people.sort_index(ascending=False)

"""
Explanation: Sorting a DataFrame
You can sort a DataFrame by calling its sort_index method. By default it sorts the rows by their index label, in ascending order, but let's reverse the order:
End of explanation
"""


people.sort_index(axis=1, inplace=True)
people

"""
Explanation: Note that sort_index returned a sorted copy of the DataFrame. To modify people directly, we can set the inplace argument to True. Also, we can sort the columns instead of the rows by setting axis=1:
End of explanation
"""


people.sort_values(by="age", inplace=True)
people

"""
Explanation: To sort the DataFrame by the values instead of the labels, we can use sort_values and specify the column to sort by:
End of explanation
"""


people.plot(kind = "line", x = "body_mass_index", y = ["height", "weight"])
plt.show()

"""
Explanation: Plotting a DataFrame
Just like for Series, pandas makes it easy to draw nice graphs based on a DataFrame.
For example, it is trivial to create a line plot from a DataFrame's data by calling its plot method:
End of explanation
"""


people.plot(kind = "scatter", x = "height", y = "weight", s=[40, 120, 200])
plt.show()

"""
Explanation: You can pass extra arguments supported by matplotlib's functions. For example, we can create scatterplot and pass it a list of sizes using the s argument of matplotlib's scatter function:
End of explanation
"""


grades_array = np.array([[8,8,9],[10,9,9],[4, 8, 2], [9, 10, 10]])
grades = pd.DataFrame(grades_array, columns=["sep", "oct", "nov"], index=["alice","bob","charles","darwin"])
grades

"""
Explanation: Again, there are way too many options to list here: the best option is to scroll through the Visualization page in pandas' documentation, find the plot you are interested in and look at the example code.
Operations on DataFrames
Although DataFrames do not try to mimick NumPy arrays, there are a few similarities. Let's create a DataFrame to demonstrate this:
End of explanation
"""


np.sqrt(grades)

"""
Explanation: You can apply NumPy mathematical functions on a DataFrame: the function is applied to all values:
End of explanation
"""


grades + 1

"""
Explanation: Similarly, adding a single value to a DataFrame will add that value to all elements in the DataFrame. This is called broadcasting:
End of explanation
"""


grades >= 5

"""
Explanation: Of course, the same is true for all other binary operations, including arithmetic (*,/,**...) and conditional (&gt;, ==...) operations:
End of explanation
"""


grades.mean()

"""
Explanation: Aggregation operations, such as computing the max, the sum or the mean of a DataFrame, apply to each column, and you get back a Series object:
End of explanation
"""


(grades > 5).all()

"""
Explanation: The all method is also an aggregation operation: it checks whether all values are True or not. Let's see during which months all students got a grade greater than 5:
End of explanation
"""


(grades > 5).all(axis = 1)

"""
Explanation: Most of these functions take an optional axis parameter which lets you specify along which axis of the DataFrame you want the operation executed. The default is axis=0, meaning that the operation is executed vertically (on each column). You can set axis=1 to execute the operation horizontally (on each row). For example, let's find out which students had all grades greater than 5:
End of explanation
"""


(grades == 10).any(axis = 1)

"""
Explanation: The any method returns True if any value is True. Let's see who got at least one grade 10:
End of explanation
"""


grades - grades.mean()  # equivalent to: grades - [7.75, 8.75, 7.50]

"""
Explanation: If you add a Series object to a DataFrame (or execute any other binary operation), pandas attempts to broadcast the operation to all rows in the DataFrame. This only works if the Series has the same size as the DataFrames rows. For example, let's substract the mean of the DataFrame (a Series object) from the DataFrame:
End of explanation
"""


pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)

"""
Explanation: We substracted 7.75 from all September grades, 8.75 from October grades and 7.50 from November grades. It is equivalent to substracting this DataFrame:
End of explanation
"""


grades - grades.values.mean() # substracts the global mean (8.00) from all grades

"""
Explanation: If you want to substract the global mean from every grade, here is one way to do it:
End of explanation
"""


bonus_array = np.array([[0,np.nan,2],[np.nan,1,0],[0, 1, 0], [3, 3, 0]])
bonus_points = pd.DataFrame(bonus_array, columns=["oct", "nov", "dec"], index=["bob","colin", "darwin", "charles"])
bonus_points

grades + bonus_points

"""
Explanation: Automatic alignment
Similar to Series, when operating on multiple DataFrames, pandas automatically aligns them by row index label, but also by column names. Let's create a DataFrame with bonus points for each person from October to December:
End of explanation
"""


(grades + bonus_points).fillna(0)

"""
Explanation: Looks like the addition worked in some cases but way too many elements are now empty. That's because when aligning the DataFrames, some columns and rows were only present on one side, and thus they were considered missing on the other side (NaN). Then adding NaN to a number results in NaN, hence the result.
Handling missing data
Dealing with missing data is a frequent task when working with real life data. Pandas offers a few tools to handle missing data.
Let's try to fix the problem above. For example, we can decide that missing data should result in a zero, instead of NaN. We can replace all NaN values by a any value using the fillna method:
End of explanation
"""


fixed_bonus_points = bonus_points.fillna(0)
fixed_bonus_points.insert(0, "sep", 0)
fixed_bonus_points.loc["alice"] = 0
grades + fixed_bonus_points

"""
Explanation: It's a bit unfair that we're setting grades to zero in September, though. Perhaps we should decide that missing grades are missing grades, but missing bonus points should be replaced by zeros:
End of explanation
"""


bonus_points

"""
Explanation: That's much better: although we made up some data, we have not been too unfair.
Another way to handle missing data is to interpolate. Let's look at the bonus_points DataFrame again:
End of explanation
"""


bonus_points.interpolate(axis=1)

"""
Explanation: Now let's call the interpolate method. By default, it interpolates vertically (axis=0), so let's tell it to interpolate horizontally (axis=1).
End of explanation
"""


better_bonus_points = bonus_points.copy()
better_bonus_points.insert(0, "sep", 0)
better_bonus_points.loc["alice"] = 0
better_bonus_points = better_bonus_points.interpolate(axis=1)
better_bonus_points

"""
Explanation: Bob had 0 bonus points in October, and 2 in December. When we interpolate for November, we get the mean: 1 bonus point. Colin had 1 bonus point in November, but we do not know how many bonus points he had in September, so we cannot interpolate, this is why there is still a missing value in October after interpolation. To fix this, we can set the September bonus points to 0 before interpolation.
End of explanation
"""


grades + better_bonus_points

"""
Explanation: Great, now we have reasonable bonus points everywhere. Let's find out the final grades:
End of explanation
"""


grades["dec"] = np.nan
final_grades = grades + better_bonus_points
final_grades

"""
Explanation: It is slightly annoying that the September column ends up on the right. This is because the DataFrames we are adding do not have the exact same columns (the grades DataFrame is missing the "dec" column), so to make things predictable, pandas orders the final columns alphabetically. To fix this, we can simply add the missing column before adding:
End of explanation
"""


final_grades_clean = final_grades.dropna(how="all")
final_grades_clean

"""
Explanation: There's not much we can do about December and Colin: it's bad enough that we are making up bonus points, but we can't reasonably make up grades (well I guess some teachers probably do). So let's call the dropna method to get rid of rows that are full of NaNs:
End of explanation
"""


final_grades_clean = final_grades_clean.dropna(axis=1, how="all")
final_grades_clean

"""
Explanation: Now let's remove columns that are full of NaNs by setting the axis argument to 1:
End of explanation
"""


final_grades["hobby"] = ["Biking", "Dancing", np.nan, "Dancing", "Biking"]
final_grades

"""
Explanation: Aggregating with groupby
Similar to the SQL language, pandas allows grouping your data into groups to run calculations over each group.
First, let's add some extra data about each person so we can group them, and let's go back to the final_grades DataFrame so we can see how NaN values are handled:
End of explanation
"""


grouped_grades = final_grades.groupby("hobby")
grouped_grades

"""
Explanation: Now let's group data in this DataFrame by hobby:
End of explanation
"""


grouped_grades.mean()

"""
Explanation: We are ready to compute the average grade per hobby:
End of explanation
"""


bonus_points

more_grades = final_grades_clean.stack().reset_index()
more_grades.columns = ["name", "month", "grade"]
more_grades["bonus"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]
more_grades

"""
Explanation: That was easy! Note that the NaN values have simply been skipped when computing the means.
Pivot tables
Pandas supports spreadsheet-like pivot tables that allow quick data summarization. To illustrate this, let's create a simple DataFrame:
End of explanation
"""


pd.pivot_table(more_grades, index="name")

"""
Explanation: Now we can call the pivot_table function for this DataFrame, asking to group by the name column. By default, pivot_table computes the mean of each numeric column:
End of explanation
"""


pd.pivot_table(more_grades, index="name", values=["grade","bonus"], aggfunc=np.max)

"""
Explanation: We can change the aggregation function by setting the aggfunc attribute, and we can also specify the list of columns whose values will be aggregated:
End of explanation
"""


pd.pivot_table(more_grades, index="name", values="grade", columns="month", margins=True)

"""
Explanation: We can also specify the columns to aggregate over horizontally, and request the grand totals for each row and column by setting margins=True:
End of explanation
"""


pd.pivot_table(more_grades, index=("name", "month"), margins=True)

"""
Explanation: Finally, we can specify multiple index or column names, and pandas will create multi-level indices:
End of explanation
"""


much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))
large_df = pd.DataFrame(much_data, columns=list("ABCDEFGHIJKLMNOPQRSTUVWXYZ"))
large_df[large_df % 16 == 0] = np.nan
large_df.insert(3,"some_text", "Blabla")
large_df

"""
Explanation: Overview functions
When dealing with large DataFrames, it is useful to get a quick overview of its content. Pandas offers a few functions for this. First, let's create a large DataFrame with a mix of numeric values, missing values and text values. Notice how Jupyter displays only the corners of the DataFrame:
End of explanation
"""


large_df.head()

"""
Explanation: The head method returns the top 5 rows:
End of explanation
"""


large_df.tail(n=2)

"""
Explanation: Of course there's also a tail function to view the bottom 5 rows. You can pass the number of rows you want:
End of explanation
"""


large_df.info()

"""
Explanation: The info method prints out a summary of each columns contents:
End of explanation
"""


large_df.describe()

"""
Explanation: Finally, the describe method gives a nice overview of the main aggregated values over each column:
* count: number of non-null (not NaN) values
* mean: mean of non-null values
* std: standard deviation of non-null values
* min: minimum of non-null values
* 25%, 50%, 75%: 25th, 50th and 75th percentile of non-null values
* max: maximum of non-null values
End of explanation
"""


my_df = pd.DataFrame(
    [["Biking", 68.5, 1985, np.nan], ["Dancing", 83.1, 1984, 3]], 
    columns=["hobby","weight","birthyear","children"],
    index=["alice", "bob"]
)
my_df

"""
Explanation: Saving & loading
Pandas can save DataFrames to various backends, including file formats such as CSV, Excel, JSON, HTML and HDF5, or to a SQL database. Let's create a DataFrame to demonstrate this:
End of explanation
"""


my_df.to_csv("my_df.csv")
my_df.to_html("my_df.html")
my_df.to_json("my_df.json")

"""
Explanation: Saving
Let's save it to CSV, HTML and JSON:
End of explanation
"""


for filename in ("my_df.csv", "my_df.html", "my_df.json"):
    print("#", filename)
    with open(filename, "rt") as f:
        print(f.read())
        print()


"""
Explanation: Done! Let's take a peek at what was saved:
End of explanation
"""


try:
    my_df.to_excel("my_df.xlsx", sheet_name='People')
except ImportError as e:
    print(e)

"""
Explanation: Note that the index is saved as the first column (with no name) in a CSV file, as &lt;th&gt; tags in HTML and as keys in JSON.
Saving to other formats works very similarly, but some formats require extra libraries to be installed. For example, saving to Excel requires the openpyxl library:
End of explanation
"""


my_df_loaded = pd.read_csv("my_df.csv", index_col=0)
my_df_loaded

"""
Explanation: Loading
Now let's load our CSV file back into a DataFrame:
End of explanation
"""


us_cities = None
try:
    csv_url = "http://simplemaps.com/files/cities.csv"
    us_cities = pd.read_csv(csv_url, index_col=0)
    us_cities = us_cities.head()
except IOError as e:
    print(e)
us_cities

"""
Explanation: As you might guess, there are similar read_json, read_html, read_excel functions as well.  We can also read data straight from the Internet. For example, let's load all U.S. cities from simplemaps.com:
End of explanation
"""


city_loc = pd.DataFrame(
    [
        ["CA", "San Francisco", 37.781334, -122.416728],
        ["NY", "New York", 40.705649, -74.008344],
        ["FL", "Miami", 25.791100, -80.320733],
        ["OH", "Cleveland", 41.473508, -81.739791],
        ["UT", "Salt Lake City", 40.755851, -111.896657]
    ], columns=["state", "city", "lat", "lng"])
city_loc

city_pop = pd.DataFrame(
    [
        [808976, "San Francisco", "California"],
        [8363710, "New York", "New-York"],
        [413201, "Miami", "Florida"],
        [2242193, "Houston", "Texas"]
    ], index=[3,4,5,6], columns=["population", "city", "state"])
city_pop

"""
Explanation: There are more options available, in particular regarding datetime format. Check out the documentation for more details.
Combining DataFrames
SQL-like joins
One powerful feature of pandas is it's ability to perform SQL-like joins on DataFrames. Various types of joins are supported: inner joins, left/right outer joins and full joins. To illustrate this, let's start by creating a couple simple DataFrames:
End of explanation
"""


pd.merge(left=city_loc, right=city_pop, on="city")

"""
Explanation: Now let's join these DataFrames using the merge function:
End of explanation
"""


all_cities = pd.merge(left=city_loc, right=city_pop, on="city", how="outer")
all_cities

"""
Explanation: Note that both DataFrames have a column named state, so in the result they got renamed to state_x and state_y.
Also, note that Cleveland, Salt Lake City and Houston were dropped because they don't exist in both DataFrames. This is the equivalent of a SQL INNER JOIN. If you want a FULL OUTER JOIN, where no city gets dropped and NaN values are added, you must specify how="outer":
End of explanation
"""


pd.merge(left=city_loc, right=city_pop, on="city", how="right")

"""
Explanation: Of course LEFT OUTER JOIN is also available by setting how="left": only the cities present in the left DataFrame end up in the result. Similarly, with how="right" only cities in the right DataFrame appear in the result. For example:
End of explanation
"""


city_pop2 = city_pop.copy()
city_pop2.columns = ["population", "name", "state"]
pd.merge(left=city_loc, right=city_pop2, left_on="city", right_on="name")

"""
Explanation: If the key to join on is actually in one (or both) DataFrame's index, you must use left_index=True and/or right_index=True. If the key column names differ, you must use left_on and right_on. For example:
End of explanation
"""


result_concat = pd.concat([city_loc, city_pop])
result_concat

"""
Explanation: Concatenation
Rather than joining DataFrames, we may just want to concatenate them. That's what concat is for:
End of explanation
"""


result_concat.loc[3]

"""
Explanation: Note that this operation aligned the data horizontally (by columns) but not vertically (by rows). In this example, we end up with multiple rows having the same index (eg. 3). Pandas handles this rather gracefully:
End of explanation
"""


pd.concat([city_loc, city_pop], ignore_index=True)

"""
Explanation: Or you can tell pandas to just ignore the index:
End of explanation
"""


pd.concat([city_loc, city_pop], join="inner")

"""
Explanation: Notice that when a column does not exist in a DataFrame, it acts as if it was filled with NaN values. If we set join="inner", then only columns that exist in both DataFrames are returned:
End of explanation
"""


pd.concat([city_loc, city_pop], axis=1)

"""
Explanation: You can concatenate DataFrames horizontally instead of vertically by setting axis=1:
End of explanation
"""


pd.concat([city_loc.set_index("city"), city_pop.set_index("city")], axis=1)

"""
Explanation: In this case it really does not make much sense because the indices do not align well (eg. Cleveland and San Francisco end up on the same row, because they shared the index label 3). So let's reindex the DataFrames by city name before concatenating:
End of explanation
"""


city_loc.append(city_pop)

"""
Explanation: This looks a lot like a FULL OUTER JOIN, except that the state columns were not renamed to state_x and state_y, and the city column is now the index.
The append method is a useful shorthand for concatenating DataFrames vertically:
End of explanation
"""


city_eco = city_pop.copy()
city_eco["eco_code"] = [17, 17, 34, 20]
city_eco

"""
Explanation: As always in pandas, the append method does not actually modify city_loc: it works on a copy and returns the modified copy.
Categories
It is quite frequent to have values that represent categories, for example 1 for female and 2 for male, or "A" for Good, "B" for Average, "C" for Bad. These categorical values can be hard to read and cumbersome to handle, but fortunately pandas makes it easy. To illustrate this, let's take the city_pop DataFrame we created earlier, and add a column that represents a category:
End of explanation
"""


city_eco["economy"] = city_eco["eco_code"].astype('category')
city_eco["economy"].cat.categories

"""
Explanation: Right now the eco_code column is full of apparently meaningless codes. Let's fix that. First, we will create a new categorical column based on the eco_codes:
End of explanation
"""


city_eco["economy"].cat.categories = ["Finance", "Energy", "Tourism"]
city_eco

"""
Explanation: Now we can give each category a meaningful name:
End of explanation
"""


city_eco.sort_values(by="economy", ascending=False)

"""
Explanation: Note that categorical values are sorted according to their categorical order, not their alphabetical order:
End of explanation
"""


