In this lesson, we're digging into Pandas Series and DataFrames - the two main data types you'll work with.
You will be able to:
- Understand and explain what Pandas Series and DataFrames are and how they differ from dictionaries and lists
- Create Series & DataFrames from dictionaries and lists
- Manipulate columns in DataFrames (df.rename, df.drop)
- Manipulate the index in DataFrames (df.reindex, df.drop, df.rename)
- Manipulate column datatypes
As we'll see as we talk more about object orientated programming, using Pandas Series and DataFrames instead of built in Python datatypes can have a range of benefits. Most importantly is that Series and DataFrames have a range of built in methods which make standard practices and procedures streamlined. This includes many of the methods we have investigated such as groupby, columns and value_counts.
Lets take a little time to import the packages we need and to import and previuew a dataset...
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
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
This MTA turnstile dataset is a great place for us to get our hands dirty wrangling and cleaning some data! Here's the data dictionary if you want to know more about the data set http://web.mta.info/developers/resources/nyct/turnstile/ts_Field_Description.txt
Let's start 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 create a column indicating whether or not the stop is an N line stop.
At this point, we will need to define some functions to perform data manipulation so that we can reuse them easily. 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
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.
Sometimes, you have messy column names
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.
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'
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')
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 |
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
You can rename columns using dictionaries as follows:
df = df.rename(columns={'DATE' : 'date'})
df.head()
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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 |
You can also drop columns
df = df.drop('C/A', axis=1) #If you don't pass the axis=1 parameter, pandas will try and drop a row with the specified index
df.head()
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UNIT | SCP | STATION | LINENAME | DIVISION | date | TIME | DESC | ENTRIES | EXITS | |
---|---|---|---|---|---|---|---|---|---|---|
0 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 08/25/2018 | 00:00:00 | REGULAR | 6736067 | 2283184 |
1 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 08/25/2018 | 04:00:00 | REGULAR | 6736087 | 2283188 |
2 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 08/25/2018 | 08:00:00 | REGULAR | 6736105 | 2283229 |
3 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 08/25/2018 | 12:00:00 | REGULAR | 6736180 | 2283314 |
4 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 08/25/2018 | 16:00:00 | REGULAR | 6736349 | 2283384 |
It can also be helpful to set an index such as when graphing.
df = df.set_index('date')
df.head()
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UNIT | SCP | STATION | LINENAME | DIVISION | TIME | DESC | ENTRIES | EXITS | |
---|---|---|---|---|---|---|---|---|---|
date | |||||||||
08/25/2018 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 00:00:00 | REGULAR | 6736067 | 2283184 |
08/25/2018 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 04:00:00 | REGULAR | 6736087 | 2283188 |
08/25/2018 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 08:00:00 | REGULAR | 6736105 | 2283229 |
08/25/2018 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 12:00:00 | REGULAR | 6736180 | 2283314 |
08/25/2018 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 16:00:00 | REGULAR | 6736349 | 2283384 |
We've seen in this lesson the differences between Pandas (Series and DataFrames) and Python native (Dictionaries and Lists) data types. We've also looked at how to create the Series and DataFrames from dictionaries and lists, and how to manipulate both columns and the index in DataFrame.