Pandas is one of the most powerful library in Python which is used for high performance and speed of calculation. It is basically an open-source BSD-licensed Python library. Commonly it is used for exploratory data analysis, machine learning, data visualization in data science, and many more. It has very dynamic and easy to understand syntax which makes users jobs easier and is a boost for developers’ innovations (as pandas is a open-source library).
Let us now start with installing pandas. Following are the commands for installing pandas on Linux, windows or mac directly use:
pip install pandas
For installing pandas on anaconda environment use:
conda install pandas
Lets now load pandas library in our programming environment.
import pandas as pd
Coming to accessing month and date in pandas, this is the part of exploratory data analysis. Suppose we want to access only the month, day, or year from date, we generally use pandas.
Method 1: Use DatetimeIndex.month attribute to find the month and use DatetimeIndex.year attribute to find the year present in the Date.
df['year'] = pd.DatetimeIndex(df['Date Attribute']).year df['month'] = pd.DatetimeIndex(df['Date Attribute']).month
Here ‘df’ is the object of the dataframe of pandas, pandas is callable as ‘pd’ (as imported), ‘DatatimeIndex()’ is a function in pandas which is used to refer to the date attribute of your dataset, ‘Date Attribute’ is the date column in your data-set (It can be anything ans varies from one data-set to other), ‘year’ and ‘month’ are the attributes for referring to the year and month respectively.
Let’s now look at an example:
Code :
Python3
# import pandas libraryimport pandas as pd# dictionary of string key and list valueraw_data = {'name': ['Rutuja', 'Neeraj', 'Renna', 'Pratik'], 'age': [20, 19, 22, 21], 'favorite_color': ['blue', 'red', 'yellow', "green"], 'grade': [88, 92, 95, 70], 'birth_date': ['01-02-2000', '08-05-1997', '04-28-1996', '12-16-1995']}# create a dataframe objectdf = pd.DataFrame(raw_data, index = ['Rutuja', 'Neeraj', 'Renna', 'Pratik'])# get year from the corresponding# birth_date column valuedf['year'] = pd.DatetimeIndex(df['birth_date']).year# get month from the corresponding# birth_date column valuedf['month'] = pd.DatetimeIndex(df['birth_date']).month# Show the dataframe# by default 5 rows from topdf.head() |
Output:
So in the output it is clearly seen that the last two columns of the data-set are appended and we have separately stored the month and date using pandas.
Method 2: Use datetime.month attribute to find the month and use datetime.year attribute to find the year present in the Date .
df['year'] = df['Date Attribute'].dt.year df['month'] = df['Date Attribute'].dt.month
Here ‘df’ is the object of the dataframe of pandas, pandas is callable as ‘pd’ (as imported), datetime is callable as ‘dt’ (as imported). ‘Date Attribute’ is the date column in your data-set (It can be anything ans varies from one data-set to other), ‘year’ and ‘month’ are the attributes for referring to the year and month respectively.
Let’s now look at example:
Code:
Python3
# import required libraryimport pandas as pdimport datetime as dt# dictionary of string as key# and list as a valueraw_data = {'Leaders': ['Mahatma Gandhi', 'Jawaharlal Nehru', 'Atal Bihari Vajpayee', 'Rabindranath Tagore'], 'birth_date': ['10-02-1869', '11-14-1889', '12-25-1924', '05-07-1861']}# create a dataframe objectdf = pd.DataFrame(raw_data, index = ['Mahatma Gandhi', 'Jawaharlal Nehru', 'Atal Bihari Vajpayee', 'Rabindranath Tagore'])# get a year from corresponding# birth_date column valuedf['year'] = df['birth_date'].dt.year# get a month from corresponding# birth_date column valuedf['month'] = df['birth_date'].dt.month# show the dataframe# by default first 5 rows# from topdf.head() |
Output:
So in the output, it is clearly seen that the last two columns of the data-set are appended and we have separately stored the month and date using pandas.
