pandas_cheat_sheet_3


Productivity with pandas

unique - find unique rows

Find unique rows in dataset


In [28]:
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline

In [2]:
comp_data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
       'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
       'Sales':[200,120,340,124,243,350]}

comp_df = pd.DataFrame(comp_data)
comp_df


Out[2]:
Company Person Sales
0 GOOG Sam 200
1 GOOG Charlie 120
2 MSFT Amy 340
3 MSFT Vanessa 124
4 FB Carl 243
5 FB Sarah 350

In [3]:
# find unique company names
comp_df['Company'].unique()


Out[3]:
array(['GOOG', 'MSFT', 'FB'], dtype=object)

nunique - find number of unique rows

More efficient than finding the unique array and finding the length of it.


In [4]:
comp_df['Company'].nunique()


Out[4]:
3

value_counts - find unique values and number of occurrences


In [5]:
comp_df['Company'].value_counts()


Out[5]:
GOOG    2
FB      2
MSFT    2
Name: Company, dtype: int64

apply - batch process column values

Calling the apply() is similar to calling the map() in Python. It can apply an operation on all records of a selected column. For instance, to find the squared sales, do the following


In [6]:
comp_df['sq_sales'] = comp_df['Sales'].apply(lambda x:x*x)
comp_df


Out[6]:
Company Person Sales sq_sales
0 GOOG Sam 200 40000
1 GOOG Charlie 120 14400
2 MSFT Amy 340 115600
3 MSFT Vanessa 124 15376
4 FB Carl 243 59049
5 FB Sarah 350 122500

We can also define a function and call that within the apply() method. This can accept values of one or more columns to calculate a new column.


In [9]:
def cuber(row):
    return row['Sales'] * row['sq_sales']

comp_df['cu_sales'] = comp_df.apply(cuber, axis=1) 
#note - how the function is called as an obj
# note - how I need to set axis to 1, instead of 0 which is defualt.
comp_df


Out[9]:
Company Person Sales sq_sales cu_sales
0 GOOG Sam 200 40000 8000000
1 GOOG Charlie 120 14400 1728000
2 MSFT Amy 340 115600 39304000
3 MSFT Vanessa 124 15376 1906624
4 FB Carl 243 59049 14348907
5 FB Sarah 350 122500 42875000

sort_values - sorting rows


In [10]:
comp_df.sort_values('Sales')


Out[10]:
Company Person Sales sq_sales cu_sales
1 GOOG Charlie 120 14400 1728000
3 MSFT Vanessa 124 15376 1906624
0 GOOG Sam 200 40000 8000000
4 FB Carl 243 59049 14348907
2 MSFT Amy 340 115600 39304000
5 FB Sarah 350 122500 42875000

Note how the index remains attached to the original rows.


In [12]:
#sorting along multiple columns
comp_df.sort_values(['Company','Sales'])


Out[12]:
Company Person Sales sq_sales cu_sales
4 FB Carl 243 59049 14348907
5 FB Sarah 350 122500 42875000
1 GOOG Charlie 120 14400 1728000
0 GOOG Sam 200 40000 8000000
3 MSFT Vanessa 124 15376 1906624
2 MSFT Amy 340 115600 39304000

isnull - finding null values throughout the DataFrame


In [13]:
comp_df.isnull()


Out[13]:
Company Person Sales sq_sales cu_sales
0 False False False False False
1 False False False False False
2 False False False False False
3 False False False False False
4 False False False False False
5 False False False False False

Working with time series data

This section explains how to specify datatypes of columns while reading data and how to define column converters to ease certain data types.


In [23]:
registrant_df = pd.read_csv('./registrant.csv')
registrant_df.head()


Out[23]:
Unnamed: 0 Registration Date Country Organization Current customer? What would you like to learn?
0 0 11/08/2019 06:09 PM EST Jamaica The University of the West Indies NaN NaN
1 1 11/08/2019 06:09 PM EST Japan iLand6 Co.,Ltd. no I am interested ArcGIS.
2 2 11/08/2019 05:56 PM EST Canada Safe Software Inc yes data science workflos
3 3 11/08/2019 05:51 PM EST Canada Le Groupe GeoInfo Inc yes general information
4 4 11/08/2019 05:26 PM EST Canada Safe Software Inc. NaN NaN

The Registration Date should be of type datetime and the Current customer? should be of bool. However, are they?


In [24]:
registrant_df.dtypes


Out[24]:
Unnamed: 0                        int64
Registration Date                object
Country                          object
Organization                     object
Current customer?                object
What would you like to learn?    object
dtype: object

Everything is a generic object. Let us re-read, this time knowing what their data types should be.


In [25]:
# define a function (lambda in this case) which will convert a column to bool depending on the 
# value of the cell
convertor_fn = lambda x: x in ['Yes', 'yes', 'YES']
convertor_map = {'Current customer?': convertor_fn}

# re-read data
registrant_df2 = pd.read_csv('./registrant.csv', 
                             parse_dates=['Registration Date'], 
                             converters = convertor_map)

registrant_df2.dtypes


/Users/atma6951/anaconda3/lib/python3.7/site-packages/dateutil/parser/_parser.py:1206: UnknownTimezoneWarning: tzname EST identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.
  category=UnknownTimezoneWarning)
/Users/atma6951/anaconda3/lib/python3.7/site-packages/dateutil/parser/_parser.py:1206: UnknownTimezoneWarning: tzname EDT identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.
  category=UnknownTimezoneWarning)
Out[25]:
Unnamed: 0                                int64
Registration Date                datetime64[ns]
Country                                  object
Organization                             object
Current customer?                          bool
What would you like to learn?            object
dtype: object

Plotting time series

Now that the Registration date is datetime, we can plot the number of registrants by time. But before that, we need to set it as the index.


In [26]:
registrant_df2.set_index('Registration Date', inplace=True)
registrant_df2.drop(axis=1, columns=['Unnamed: 0'], inplace=True) # drop bad column
registrant_df2.head()


Out[26]:
Country Organization Current customer? What would you like to learn?
Registration Date
2019-11-08 18:09:00 Jamaica The University of the West Indies False NaN
2019-11-08 18:09:00 Japan iLand6 Co.,Ltd. False I am interested ArcGIS.
2019-11-08 17:56:00 Canada Safe Software Inc True data science workflos
2019-11-08 17:51:00 Canada Le Groupe GeoInfo Inc True general information
2019-11-08 17:26:00 Canada Safe Software Inc. False NaN

Sort dataframe by time


In [30]:
registrant_df2.sort_index(inplace=True)

Add a counter column to the dataframe

Note. It is important to count up only after sorting. Else the numbers are going to be all over the place.


In [31]:
registrant_df2['registration_count'] = range(1, len(registrant_df2)+1) # goes from 1 to 284

In [32]:
plt.figure(figsize=(10,7))
registrant_df2['registration_count'].plot(kind='line')
plt.title('Number of registrants over time');