I have a dataframe which looks like this:
I wanted to make a dataframe which looks like this:
For this I have referred the post at pandas convert some columns into rows.
By using the merge function I get a dataframe as shown below:
How do I get my dataframe in the format required?
The complete code is as shown:
import pandas as pd
from nsepy import get_history
from datetime import date
import numpy as np
stock = ['APLAPOLLO','AUBANK','AARTIDRUGS','AARTIIND','AAVAS','ABBOTINDIA','ADANIENT','ADANIGAS','ADANIGREEN','ADANIPORTS']
res = dict(zip(stock,stock))
start = date (2020, 11, 22)
end = date (2020, 12, 22)
for stock_name in stock:
data = get_history(symbol=stock_name, start=start, end=end)
res[stock_name]=data
for key, df in res.items():
# create a column called "key name"
df['key_name'] = key
lst = list(res.values())
df = pd.concat(lst)
df['boolean'] = df['Prev Close'] < df['Close']
df1 = pd.DataFrame({'boolean' : [True] + [False] * 2 + [True] * 3})
a = df['boolean']
b = a.cumsum()
df['trend'] = (b-b.mask(a).ffill().fillna(0).astype(int)).where(a, 0)
conditions = [(df['boolean']==True), (df['boolean']==False)]
values=['Win','Loose']
df['Win/Loss']=np.select(conditions,values)
df=df.drop(['Win/Loose'],axis=1)
df.to_csv('data.csv')
conditions = [(df['trend']>=2), df['trend']<2]
df2=df[['trend','Symbol']]
w=df2.melt(id_vars=["trend"],value_vars=['Symbol'])
