Adding to dataframes

Load the initial data.

import pandas as pd

small_url = 'https://gist.githubusercontent.com/van-alfen/bca77fd9f4c0fcf74308bc7cfa12a8a7/raw/030a8fea2ddfd1b8fd257d5a601b15a2942c78d7/small_data.csv'
small_df = pd.read_csv(small_url)
small_df.set_index('ticker', inplace=True)
small_df

	price	name	assets_current	liab_current
ticker
AAPL	137	Apple Inc	162.82	105.72
MSFT	229	Microsoft Corp	181.92	72.31
GOOG	1677	Alphabet Inc	152.58	45.22
AMZN	3535	Amazon.com Inc	96.33	87.81

Adding rows

We can append observations (rows) to a dataframe by using the .concat() method.

new_obs = pd.DataFrame(
	columns=['ticker', 'price', 'name', 'assets_current', 'liab_current'],
	data=[
		['TSLA', 1256, 'Tesla Inc', 27.1, 19.71],
	],
)
new_obs.set_index('ticker', inplace=True)
new_obs

	price	name	assets_current	liab_current
ticker
TSLA	1256	Tesla Inc	27.1	19.71

medium_df = pd.concat([small_df, new_obs])
medium_df

	price	name	assets_current	liab_current
ticker
AAPL	137	Apple Inc	162.82	105.72
MSFT	229	Microsoft Corp	181.92	72.31
GOOG	1677	Alphabet Inc	152.58	45.22
AMZN	3535	Amazon.com Inc	96.33	87.81
TSLA	1256	Tesla Inc	27.10	19.71

Adding columns

Create a new column named nwc that is equal to current assets minus current liabilities.

small_df['nwc'] = small_df['assets_current'] - small_df['liab_current']
small_df

	price	name	assets_current	liab_current	nwc
ticker
AAPL	137	Apple Inc	162.82	105.72	57.10
MSFT	229	Microsoft Corp	181.92	72.31	109.61
GOOG	1677	Alphabet Inc	152.58	45.22	107.36
AMZN	3535	Amazon.com Inc	96.33	87.81	8.52

Add a new expensive column that is equal to ‘yes’ if the price is higher than $1,000 and ‘no’ otherwise.

In this case we can use a filter inside .loc to set values in a new column.

expensive_rows_filter = ( small_df['price'] >  1000 )
cheap_rows_filter     = ( small_df['price'] <= 1000 )

small_df.loc[ expensive_rows_filter, 'expensive' ] = 'yes'
small_df.loc[ cheap_rows_filter,     'expensive' ] = 'no'
small_df

	price	name	assets_current	liab_current	nwc	expensive
ticker
AAPL	137	Apple Inc	162.82	105.72	57.10	no
MSFT	229	Microsoft Corp	181.92	72.31	109.61	no
GOOG	1677	Alphabet Inc	152.58	45.22	107.36	yes
AMZN	3535	Amazon.com Inc	96.33	87.81	8.52	yes