Business Days with Custom Holidays

30 May 2019
python

Importing Packages and Datasets

import pandas as pd

start_date = ['2019-06-03', '2019-06-13', '2019-10-01', '2019-09-01']
end_date =  ['2019-08-31', '2019-06-21', '2019-10-25', '2019-12-25']
data = pd.DataFrame(list(zip(start_date,end_date)), columns = ['Start Date', 'End Date'])
data

	Start Date	End Date
0	2019-06-03	2019-08-31
1	2019-06-13	2019-06-21
2	2019-10-01	2019-10-25
3	2019-09-01	2019-12-25

Custm Holidays List

holiday_dates = [pd.datetime(2019, 8, 15), pd.datetime(2019, 10, 2), pd.datetime(2019, 10, 8),
                 pd.datetime(2019, 10, 28), pd.datetime(2019, 12, 25)]

Applying functions over pandas dataframe using apply, applymap and map

23 Apr 2019
python

Importing Packages and Datasets

import pandas as pd
import numpy as np
data = pd.DataFrame(np.random.rand(4, 3)*100,
                    columns=['Physics','Çhemistry','Maths'],
                    index = ['Student 1', 'Student 2','Student 3','Student 4'])
data

	Physics	Çhemistry	Maths
Student 1	59.488944	14.888411	52.794760
Student 2	21.872113	66.481646	87.190572
Student 3	9.885919	54.449674	58.696036
Student 4	33.804378	6.286295	30.373699

Applymap

RoundUpto2Decimal = lambda x: round(x,2)
data.applymap(RoundUpto2Decimal)

	Physics	Çhemistry	Maths
Student 1	59.49	14.89	52.79
Student 2	21.87	66.48	87.19
Student 3	9.89	54.45	58.70
Student 4	33.80	6.29	30.37

Indexing and Sorting a dataframe using iloc and loc

08 Apr 2019
python

There are multiple ways in pandas by which a dataframe can be indexed i.e, selecting particular set of rows and columns from a dataframe. For a detailed description over this topic, once can refer official pandas documentation - Indexing and Selecting Data

We’ll discuss the following -

Let’s begin with loading a sample dataset and required python packages.

import pandas as pd
import numpy as np
#Fetching data from url as csv by mentioning values of various paramters
data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                   header = None,
                   index_col = False,
                   names = ['sepal_length','sepal_width','petal_length','petal_width','iris_class'])
# visualising first five rows of sample dataset (Iris)
data.head()

The sample first five rows of data looks like (can be viewed using data.head())-

	sepal_length	sepal_width	petal_length	petal_width	iris_class
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa