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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:
 Software Engineering for Data Scientists
Sophisticated Data Manipulation
DATA 515 A
1. Python's Data Science Ecosystem
With this simple Python computation experience under our belt, we can now move to doing some more interesting analysis.
Python's Data Science Ecosystem
In addition to Python's built-in modules like the math module we explored above, there are also many often-used third-party modules that are core tools for doing data science with Python.
Some of the most important ones are
Step1:  Lists in native Python
Let's create a list, a native Python object that we've used earlier today.
Step2:  This list is one-dimensional, let's make it multidimensional!
Step3:  How do we access the 6 element in the second row, third column for native Python list?
Step4:  Converting to numpy Arrays
Step5:  How do we access the 6 element in the second row, third column for numpy array?
Step6:  How do we retrieve a slice of the array, array([[1, 2], [4, 5]])?
Step7:  How do we retrieve the second column of the array?
Step8:  4. Introduction to Pandas DataFrames
What are the elements of a table?
Step9:  What operations do we perform on tables?
Step10:  Operations on a Pandas DataFrame
5. Manipulating Data with DataFrames
Downloading the data
Shell commands can be run from the notebook by preceding them with an exclamation point
Step11:  uncomment this to download the data
Step12:  Loading Data into a DataFrame
Because we'll use it so much, we often import under a shortened name using the import ... as ... pattern
Step13:  Now we can use the read_csv command to read the comma-separated-value data
Step14:  The shape attribute shows us the number of elements
Step15:  The columns attribute gives us the column names
The index attribute gives us the index names
The dtypes attribute gives the data types of each column
Step16:  Sophisticated Data Manipulation
Here we'll cover some key features of manipulating data with pandas
Access columns by name using square-bracket indexing
Step17:  Mathematical operations on columns happen element-wise
Step18:  Columns can be created (or overwritten) with the assignment operator.
Let's create a tripminutes column with the number of minutes for each trip
More complicated mathematical operations can be done with tools in the numpy package
Step19:  Or to break down rides by age
Step20:  By default, the values rather than the index are sorted. Use sort=False to turn this behavior off
Step21:  We can explore other things as well
Step22:  Group-by Operation
One of the killer features of the Pandas dataframe is the ability to do group-by operations.
You can visualize the group-by like this (image borrowed from the Python Data Science Handbook)
Step23:  The simplest version of a groupby looks like this, and you can use almost any aggregation function you wish (mean, median, sum, minimum, maximum, standard deviation, count, etc.)
&lt;data object&gt;.groupby(&lt;grouping values&gt;).&lt;aggregate&gt;()
for example, we can group by gender and find the average of all numerical columns
Step24:  It's also possible to index the grouped object like it is a dataframe
Step25:  Now we can simply call the plot() method of any series or dataframe to get a reasonable view of the data
Step26:  Adjusting the Plot Style
Matplotlib has a number of plot styles you can use. For example, if you like R you might use the ggplot style
Step27:  Other plot types
Pandas supports a range of other plotting types; you can find these by using the <TAB> autocomplete on the plot method
Step28:  For example, we can create a histogram of trip durations
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Python Code:
import numpy as np
Explanation: Software Engineering for Data Scientists
Sophisticated Data Manipulation
DATA 515 A
1. Python's Data Science Ecosystem
With this simple Python computation experience under our belt, we can now move to doing some more interesting analysis.
Python's Data Science Ecosystem
In addition to Python's built-in modules like the math module we explored above, there are also many often-used third-party modules that are core tools for doing data science with Python.
Some of the most important ones are:
numpy: Numerical Python
Numpy is short for "Numerical Python", and contains tools for efficient manipulation of arrays of data.
If you have used other computational tools like IDL or MatLab, Numpy should feel very familiar.
scipy: Scientific Python
Scipy is short for "Scientific Python", and contains a wide range of functionality for accomplishing common scientific tasks, such as optimization/minimization, numerical integration, interpolation, and much more.
We will not look closely at Scipy today, but we will use its functionality later in the course.
pandas: Labeled Data Manipulation in Python
Pandas is short for "Panel Data", and contains tools for doing more advanced manipulation of labeled data in Python, in particular with a columnar data structure called a Data Frame.
If you've used the R statistical language (and in particular the so-called "Hadley Stack"), much of the functionality in Pandas should feel very familiar.
matplotlib: Visualization in Python
Matplotlib started out as a Matlab plotting clone in Python, and has grown from there in the 15 years since its creation. It is the most popular data visualization tool currently in the Python data world (though other recent packages are starting to encroach on its monopoly).
2. Installation
Installing Pandas & friends
Because the above packages are not included in Python itself, you need to install them separately. While it is possible to install these from source (compiling the C and/or Fortran code that does the heavy lifting under the hood) it is much easier to use a package manager like conda. All it takes is to run
$ conda install numpy scipy pandas matplotlib
and (so long as your conda setup is working) the packages will be downloaded and installed on your system.
3. Arrays and slicing in Numpy
End of explanation
my_list = [2, 5, 7, 8]
my_list
type(my_list)
Explanation: Lists in native Python
Let's create a list, a native Python object that we've used earlier today.
End of explanation
multi_list = [[1, 2, 3], [4, 5, 6]]
Explanation: This list is one-dimensional, let's make it multidimensional!
End of explanation
#
Explanation: How do we access the 6 element in the second row, third column for native Python list?
End of explanation
my_array = np.array(my_list)
type(my_array)
my_array.dtype
multi_array.shape
multi_array = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
Explanation: Converting to numpy Arrays
End of explanation
#
Explanation: How do we access the 6 element in the second row, third column for numpy array?
End of explanation
#
Explanation: How do we retrieve a slice of the array, array([[1, 2], [4, 5]])?
End of explanation
#
Explanation: How do we retrieve the second column of the array?
End of explanation
# Pandas DataFrames as table elements
import pandas as pd
Explanation: 4. Introduction to Pandas DataFrames
What are the elements of a table?
End of explanation
df = pd.DataFrame({'A': [1,2,3], 'B': [2, 4, 6], 'ccc': [1.0, 33, 4]})
df          
sub_df = df[['A', 'ccc']]
sub_df
df['A'] + 2*df['B']
Explanation: What operations do we perform on tables?
End of explanation
!ls
Explanation: Operations on a Pandas DataFrame
5. Manipulating Data with DataFrames
Downloading the data
Shell commands can be run from the notebook by preceding them with an exclamation point:
End of explanation
!curl -o pronto.csv https://data.seattle.gov/api/views/tw7j-dfaw/rows.csv?accessType=DOWNLOAD
Explanation: uncomment this to download the data:
End of explanation
import pandas as pd
df = pd.read_csv('pronto.csv')
type(df)
len(df)
Explanation: Loading Data into a DataFrame
Because we'll use it so much, we often import under a shortened name using the import ... as ... pattern:
End of explanation
df.head()
df.columns
df.index
smaller_df = df.loc[[1,4,6,7,9,34],:]
smaller_df.index
Explanation: Now we can use the read_csv command to read the comma-separated-value data:
Note: strings in Python can be defined either with double quotes or single quotes
Viewing Pandas Dataframes
The head() and tail() methods show us the first and last rows of the data
End of explanation
df.shape
Explanation: The shape attribute shows us the number of elements:
End of explanation
df.dtypes
Explanation: The columns attribute gives us the column names
The index attribute gives us the index names
The dtypes attribute gives the data types of each column:
End of explanation
df_small = df['stoptime']
type(df_small)
df_small.tolist()
Explanation: Sophisticated Data Manipulation
Here we'll cover some key features of manipulating data with pandas
Access columns by name using square-bracket indexing:
End of explanation
trip_duration_hours = df['tripduration']/3600
trip_duration_hours[:2]
trip_duration_hours.head()
df['trip_duration_hours'] = df['tripduration']/3600
del df['trip_duration_hours']
df.head()
df.loc[[0,1],:]
df_long_trips = df[df['tripduration'] >10000]
sel = df['tripduration'] > 10000
df_long_trips = df[sel]
df_long_trips
df[sel].shape
# Make a copy of a slice
df_subset = df[['starttime', 'stoptime']].copy()
df_subset['trip_hours'] = df['tripduration']/3600
Explanation: Mathematical operations on columns happen element-wise:
End of explanation
pd.value_counts(df["gender"])
Explanation: Columns can be created (or overwritten) with the assignment operator.
Let's create a tripminutes column with the number of minutes for each trip
More complicated mathematical operations can be done with tools in the numpy package:
Working with Times
One trick to know when working with columns of times is that Pandas DateTimeIndex provides a nice interface for working with columns of times.
For a dataset of this size, using pd.to_datetime and specifying the date format can make things much faster (from the strftime reference, we see that the pronto data has format "%m/%d/%Y %I:%M:%S %p"
(Note: you can also use infer_datetime_format=True in most cases to automatically infer the correct format, though due to a bug it doesn't work when AM/PM are present)
With it, we can extract, the hour of the day, the day of the week, the month, and a wide range of other views of the time:
Simple Grouping of Data
The real power of Pandas comes in its tools for grouping and aggregating data. Here we'll look at value counts and the basics of group-by operations.
Value Counts
Pandas includes an array of useful functionality for manipulating and analyzing tabular data.
We'll take a look at two of these here.
The pandas.value_counts returns statistics on the unique values within each column.
We can use it, for example, to break down rides by gender:
End of explanation
pd.value_counts(2019 - df["birthyear"])
Explanation: Or to break down rides by age:
End of explanation
pd.value_counts(df["birthyear"], sort=False)
Explanation: By default, the values rather than the index are sorted. Use sort=False to turn this behavior off:
End of explanation
#
Explanation: We can explore other things as well: day of week, hour of day, etc.
End of explanation
df.head()
df_count = df.groupby(['from_station_id']).count()
df_count.head()
df_mean = df.groupby(['from_station_id']).mean()
df_mean.head()
dfgroup = df.groupby(['from_station_id'])
dfgroup.groups
Explanation: Group-by Operation
One of the killer features of the Pandas dataframe is the ability to do group-by operations.
You can visualize the group-by like this (image borrowed from the Python Data Science Handbook)
End of explanation
df.groupby(gender).mean()
Explanation: The simplest version of a groupby looks like this, and you can use almost any aggregation function you wish (mean, median, sum, minimum, maximum, standard deviation, count, etc.)
&lt;data object&gt;.groupby(&lt;grouping values&gt;).&lt;aggregate&gt;()
for example, we can group by gender and find the average of all numerical columns:
End of explanation
%matplotlib inline
Explanation: It's also possible to index the grouped object like it is a dataframe:
You can even group by multiple values: for example we can look at the trip duration by time of day and by gender:
The unstack() operation can help make sense of this type of multiply-grouped data. What this technically does is split a multiple-valued index into an index plus columns:
Visualizing data with pandas
Of course, looking at tables of data is not very intuitive.
Fortunately Pandas has many useful plotting functions built-in, all of which make use of the matplotlib library to generate plots.
Whenever you do plotting in the IPython notebook, you will want to first run this magic command which configures the notebook to work well with plots:
End of explanation
import matplotlib.pyplot as plt
df['tripduration'].hist()
Explanation: Now we can simply call the plot() method of any series or dataframe to get a reasonable view of the data:
End of explanation
plt.style.use("ggplot")
Explanation: Adjusting the Plot Style
Matplotlib has a number of plot styles you can use. For example, if you like R you might use the ggplot style:
End of explanation
plt.
Explanation: Other plot types
Pandas supports a range of other plotting types; you can find these by using the <TAB> autocomplete on the plot method:
End of explanation
# A script for creating a dataframe with counts of the occurrence of a columns' values
df_count = df.groupby('from_station_id').count()
df_count1 = df_count[['trip_id']]
df_count2 = df_count1.rename(columns={'trip_id': 'count'})
df_count2.head()
def make_table_count(df_arg, groupby_column):
    df_count = df_arg.groupby(groupby_column).count()
    column_name = df.columns[0]
    df_count1 = df_count[[column_name]]
    df_count2 = df_count1.rename(columns={column_name: 'count'})
    return df_count2
dff = make_table_count(df, 'from_station_id')
dff.head()
Explanation: For example, we can create a histogram of trip durations:
If you'd like to adjust the x and y limits of the plot, you can use the set_xlim() and set_ylim() method of the resulting object:
Breakout: Exploring the Data
Make a plot of the total number of rides as a function of month of the year (You'll need to extract the month, use a groupby, and find the appropriate aggregation to count the number in each group).
Split this plot by gender. Do you see any seasonal ridership patterns by gender?
Split this plot by user type. Do you see any seasonal ridership patterns by usertype?
Repeat the above three steps, counting the number of rides by time of day rather that by month.
Are there any other interesting insights you can discover in the data using these tools?
Using Files
Writing and running python modules
Using python modules in your Jupyter Notebook
End of explanation
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