learn-co-students / ds-skills-pandas-cleaning-qa-internal

Import pandas and other packages to be used

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

Import and preview a dataset

df = pd.read_csv('turnstile_180901.txt')
print(len(df))
df.head()

197625

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	C/A	UNIT	SCP	STATION	LINENAME	DIVISION	DATE	TIME	DESC	ENTRIES	EXITS
0	A002	R051	02-00-00	59 ST	NQR456W	BMT	08/25/2018	00:00:00	REGULAR	6736067	2283184
1	A002	R051	02-00-00	59 ST	NQR456W	BMT	08/25/2018	04:00:00	REGULAR	6736087	2283188
2	A002	R051	02-00-00	59 ST	NQR456W	BMT	08/25/2018	08:00:00	REGULAR	6736105	2283229
3	A002	R051	02-00-00	59 ST	NQR456W	BMT	08/25/2018	12:00:00	REGULAR	6736180	2283314
4	A002	R051	02-00-00	59 ST	NQR456W	BMT	08/25/2018	16:00:00	REGULAR	6736349	2283384

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 197625 entries, 0 to 197624
Data columns (total 12 columns):
C/A                                                                     197625 non-null object
UNIT                                                                    197625 non-null object
SCP                                                                     197625 non-null object
STATION                                                                 197625 non-null object
LINENAME                                                                197625 non-null object
DIVISION                                                                197625 non-null object
DATE                                                                    197625 non-null object
TIME                                                                    197625 non-null object
DESC                                                                    197625 non-null object
ENTRIES                                                                 197625 non-null int64
EXITS                                                                   197625 non-null int64
On_N_Line                                                               197625 non-null bool
dtypes: bool(1), int64(2), object(9)
memory usage: 16.8+ MB

Data Munging/ Manipulation

This MTA turnstile dataset is a great place for us to get our hands dirty wrnagling and cleaning some data!

Let's start by example by filtering the data down to all stations for the N line. To do this, we'll need to extract all "N"s from the LINENAME column, or similarly, create a column indicating whether or not the stop is an N line stop.

Defining Functions

At this point, we will need to define some functions to perform data manipulation. In python, we define a function using the def keyword. Afterwords, we give the function a name, followed by parentheses. Any required (or optional parameters) are specified within the parentheses, just as you would normally call a function. You then specify the functions behavior using a colon and an indendation, much the same way you would a for loop or conditional block. Finally, if you want your function to return something (as with the str.pop() method) as opposed to a function that simply does something in the background but returns nothing (such as list.append()), you must use the return keyword. Note that as soon as a function hits a point in execution where something is returned, the function would terminate and no further commands would be executed. In other words the return command both returns a value and forces termination of the function.

def contains_n(text):
    if 'N' in text:
        return True
    else:
        return False

#or the shorter, more pythonic:
def contains_n(text):
    bool_val = 'N' in text
    return bool_val

df['On_N_Line'] = df.LINENAME.map(contains_n)
df.head(2)

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	C/A	UNIT	SCP	STATION	LINENAME	DIVISION	DATE	TIME	DESC	ENTRIES	EXITS	On_N_Line
0	A002	R051	02-00-00	59 ST	NQR456W	BMT	08/25/2018	00:00:00	REGULAR	6736067	2283184	True
1	A002	R051	02-00-00	59 ST	NQR456W	BMT	08/25/2018	04:00:00	REGULAR	6736087	2283188	True

df.tail(2)

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	C/A	UNIT	SCP	STATION	LINENAME	DIVISION	DATE	TIME	DESC	ENTRIES	EXITS	On_N_Line
197623	TRAM2	R469	00-05-01	RIT-ROOSEVELT	R	RIT	08/31/2018	17:00:00	REGULAR	5554	348	False
197624	TRAM2	R469	00-05-01	RIT-ROOSEVELT	R	RIT	08/31/2018	21:00:00	REGULAR	5554	348	False

df.On_N_Line.value_counts(normalize=True)

False    0.870441
True     0.129559
Name: On_N_Line, dtype: float64

Explanation

Above we used the map method for pandas series. This allows us to pass a function that will be applied to each and every data entry within the series. As shorthand, we could also pass a lambda function to determine whether or not each row was on the N line or not.
df['On_N_Line'] = df.LINENAME.map(lambda x: 'N' in x) This is shorter and equivalent to the above functions defined above. Lambda functions are often more covenient shorthand, but have less functionality then defining functions explicitly.

Practice

Create another column 'Num_Lines' that is a count of how many lines pass through a station. Then sort your dataframe by this column in descending order.

# Your code here

Cleaning Column Names

Sometimes, you have messy column names. Observe:

df.columns

Index(['C/A', 'UNIT', 'SCP', 'STATION', 'LINENAME', 'DIVISION', 'DATE', 'TIME',
       'DESC', 'ENTRIES',
       'EXITS                                                               ',
       'On_N_Line'],
      dtype='object')

You might notice that foolishly, the EXITS column has a lot of annoying whitespace following it. We can quickly use a list comprehension to clean up all of the column names.

Write a function to clean a column name.

def clean(col_name):
    cleaned = #Your code here; whatever you want to do to col_name. Hint: think back to str methods.
    return cleaned

#This is a list comprehension. It applies your clean function to every item in the list.
#We then reassign that to df.columns
#You shouldn't have to change anything here.
#Your function above should work appropriately here.
df.columns = [clean(col) for col in df.columns] 

#Checking the output, we can see the results.
df.columns

Reformatting Column Types

Another common data munging technique can be reformating column types. We first previewed column types above using the df.info() method, which we'll repeat here.

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 197625 entries, 0 to 197624
Data columns (total 12 columns):
C/A                                                                     197625 non-null object
UNIT                                                                    197625 non-null object
SCP                                                                     197625 non-null object
STATION                                                                 197625 non-null object
LINENAME                                                                197625 non-null object
DIVISION                                                                197625 non-null object
DATE                                                                    197625 non-null object
TIME                                                                    197625 non-null object
DESC                                                                    197625 non-null object
ENTRIES                                                                 197625 non-null int64
EXITS                                                                   197625 non-null int64
On_N_Line                                                               197625 non-null bool
dtypes: bool(1), int64(2), object(9)
memory usage: 16.8+ MB

A common transformation needed is converting numbers stored as text to float or integer representations. In this cas ENTRIES and EXITS are appropriately int64, but to practice, we'll demonstrate changing that to a float and then back to an int.

print(df.ENTRIES.dtype) #We can also check an individual column type rather then all 
df.ENTRIES = df.ENTRIES.astype(float) #Changing the column to float
print(df.ENTRIES.dtype) #Checking our changes

int64
float64

#Converting Back
print(df.ENTRIES.dtype) 
df.ENTRIES = df.ENTRIES.astype(int)
print(df.ENTRIES.dtype)

float64
int64

Attempting to convert a string column to int or float will produce errors if there are actually non numeric characters

df.LINENAME = df.LINENAME.astype(int)

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-15-9635123507d4> in <module>()
----> 1 df.LINENAME = df.LINENAME.astype(int)


~/anaconda3/lib/python3.6/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
    175                 else:
    176                     kwargs[new_arg_name] = new_arg_value
--> 177             return func(*args, **kwargs)
    178         return wrapper
    179     return _deprecate_kwarg


~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors, **kwargs)
   4995             # else, only a single dtype is given
   4996             new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,
-> 4997                                          **kwargs)
   4998             return self._constructor(new_data).__finalize__(self)
   4999 


~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in astype(self, dtype, **kwargs)
   3712 
   3713     def astype(self, dtype, **kwargs):
-> 3714         return self.apply('astype', dtype=dtype, **kwargs)
   3715 
   3716     def convert(self, **kwargs):


~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
   3579 
   3580             kwargs['mgr'] = self
-> 3581             applied = getattr(b, f)(**kwargs)
   3582             result_blocks = _extend_blocks(applied, result_blocks)
   3583 


~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in astype(self, dtype, copy, errors, values, **kwargs)
    573     def astype(self, dtype, copy=False, errors='raise', values=None, **kwargs):
    574         return self._astype(dtype, copy=copy, errors=errors, values=values,
--> 575                             **kwargs)
    576 
    577     def _astype(self, dtype, copy=False, errors='raise', values=None,


~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in _astype(self, dtype, copy, errors, values, klass, mgr, **kwargs)
    662 
    663                 # _astype_nansafe works fine with 1-d only
--> 664                 values = astype_nansafe(values.ravel(), dtype, copy=True)
    665                 values = values.reshape(self.shape)
    666 


~/anaconda3/lib/python3.6/site-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy)
    707         # work around NumPy brokenness, #1987
    708         if np.issubdtype(dtype.type, np.integer):
--> 709             return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
    710 
    711         # if we have a datetime/timedelta array of objects


pandas/_libs/lib.pyx in pandas._libs.lib.astype_intsafe()


pandas/_libs/src/util.pxd in util.set_value_at_unsafe()


ValueError: invalid literal for int() with base 10: 'NQR456W'

Converting Dates

A slightly more complicated data type transformation is creating date or datetime objects. These are built in datatypes that have useful information such as being able to quickly calculate the time between two days, or extracting the day of the week from a given date. However, if we look at our current date column, we will notice it is simply a non-null object (probably simply text).

df.DATE.dtype

dtype('O')

pd.to_datetime()

This is the handiest of methods when converting strings to datetime objects.

#Often you can simply pass the series into this method.
pd.to_datetime(df.DATE).head() #It is good practice to preview the results first
#This prevents overwriting data if some error was produced. However everything looks good!

0   2018-08-25
1   2018-08-25
2   2018-08-25
3   2018-08-25
4   2018-08-25
Name: DATE, dtype: datetime64[ns]

Sometimes the above won't work and you'll have to explicitly pass how the date is formatted.
To do that, you have to use some datetime codes. Here's a preview of some of the most common ones:

To explicitly pass formatting parameters, preview your dates and write the appropriate codes.

df.DATE.iloc[0] #Another method for slicing series/dataframes

'08/25/2018'

#Notice we include delimiters (in this case /) between the codes.
pd.to_datetime(df.DATE, format='%m/%d/%Y').head()

0   2018-08-25
1   2018-08-25
2   2018-08-25
3   2018-08-25
4   2018-08-25
Name: DATE, dtype: datetime64[ns]

#Actually apply and save our changes
df.DATE = pd.to_datetime(df.DATE)
print(df.DATE.dtype)
#Preview updated dataframe
df.head(2)

datetime64[ns]

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	C/A	UNIT	SCP	STATION	LINENAME	DIVISION	DATE	TIME	DESC	ENTRIES	EXITS	On_N_Line
0	A002	R051	02-00-00	59 ST	NQR456W	BMT	2018-08-25	00:00:00	REGULAR	6736067	2283184	True
1	A002	R051	02-00-00	59 ST	NQR456W	BMT	2018-08-25	04:00:00	REGULAR	6736087	2283188	True

Datetime Methods

Now that we have converted the DATE field to a datetime object we can use some useful built in methods.

#dt stores all the built in datetime methods (only works for datetime columns)
df.DATE.dt.day_name().head()

0    Saturday
1    Saturday
2    Saturday
3    Saturday
4    Saturday
Name: DATE, dtype: object

Groupby Methods

Let's look at one more extremely important method. Groupby allows you to aggregate the data based on a field. You then apply an aggregation function such as mean, median, count, min, max, or even a custom function which you can define yourself!

df.groupby('STATION').sum().head()

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	ENTRIES	EXITS	On_N_Line
STATION
1 AV	127637422994	68336694378	0.0
103 ST	6809043268	26408857862	0.0
103 ST-CORONA	2936772648	3007136644	0.0
104 ST	201698739525	146808927663	0.0
110 ST	1485948761	1430806219	0.0

Notice that our groupby column becomes the index of the resulting view. To change this, you can also use the .reset_index() method.

df.groupby('STATION').sum().reset_index().head()

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	STATION	ENTRIES	EXITS	On_N_Line
0	1 AV	127637422994	68336694378	0.0
1	103 ST	6809043268	26408857862	0.0
2	103 ST-CORONA	2936772648	3007136644	0.0
3	104 ST	201698739525	146808927663	0.0
4	110 ST	1485948761	1430806219	0.0

Finally, we could graph these as before.

df.columns = [col.strip() for col in df.columns]

to_graph = df.groupby('STATION').sum().sort_values(by='ENTRIES')[['ENTRIES', 'EXITS']]
#Recall that default behavior for .sort_values() is ascending.
#Hence, we look at the tail rather then head for top values
#Alternatively, we could use .sort_values(ascending=False) followed by the .head() method
to_graph.tail(10).plot(kind='barh');
plt.title('Top 10 Stations Based on Number of Entries.');
plt.xlabel('Number of Entries/Exits');

Practice

Here we begin to do some exploratory analysis to answer some questions.

1. Compare subway traffic by day of the week. Display this as a graph.

#Your code here

2. Is there more subway traffic on a weekend or a weekday? Be specific in comparing magnitudes.

#Your code here