Many times we cant to create an indicator based on other values, this can be achieved very easily in pandas.
| ################################################################################################ | |
| # name: create_column_from_other_columns.py | |
| # desc: Create new columns in pandas DataFrame | |
| # date: 2018-06-16 | |
| # Author: conquistadorjd | |
| ################################################################################################ | |
| import pandas as pd | |
| import numpy as np | |
| # import pandas_datareader as datareader | |
| import matplotlib.pyplot as plt | |
| import datetime | |
| from matplotlib.finance import candlestick_ohlc | |
| # from mpl_finance import candlestick_ohlc | |
| import matplotlib.dates as mdates | |
| print('*** Program Started ***') | |
| df = pd.read_csv('15-06-2016-TO-14-06-2018HDFCBANKALLN.csv') | |
| # ensuring only equity series is considered | |
| df = df.loc[df['Series'] == 'EQ'] | |
| # Converting date to pandas datetime format | |
| df['Date'] = pd.to_datetime(df['Date']) | |
| # print(df.dtypes) | |
| # print(df.head()) | |
| #### Normail column creation | |
| df['range'] = df['High Price'] – df['Low Price'] | |
| df['Average'] = (df['Close Price'] + df['Open Price'])/2 | |
| #### Conditional Value | |
| df['GT1400'] = np.where( (df['Close Price']> 1400), 1, 0) | |
| df['DailyTrend'] = np.where( (df['Close Price']>= df['Open Price']), 'Positive', 'Negative') | |
| df['Calculated Column'] = np.where( (df['Close Price']>= df['Open Price']), df['Close Price']– df['Open Price'], (df['Close Price'] + df['Open Price'])/2) | |
| df.to_csv('hdfc_with_calculated_columns.csv') | |
| print('*** Program ended ***') |