Export specific data to csv file

Question

Beginner python. Take it slow with me please:)

I have a consolidation script that ends with 'printing' a list of files whether they are consolidating or breakout candidates. While it prints nicely I want to take those prints and create 2 separate csv files.

What the script does is it reads hundreds of CSV files in a dataset/daily folder and creates the dataframe to then figure out what stocks are consolidators or breakout candidates.

What I am trying to do is have the script EXPORT two separate lists one listing consolidators and another listing breakout stock symbols from the data but am having a ton of trouble doing so. You can see those lines at the bottom.

Any help would be greatly appreciated.

import os, pandas

def is_consolidating(df, percentage=2.5):
    recent_candlesticks = df[-15:]
    
    max_close = recent_candlesticks['Close'].max()
    min_close = recent_candlesticks['Close'].min()

    threshold = 1 - (percentage / 100)
    if min_close > (max_close * threshold):
        return True        

    return False

def is_breaking_out(df, percentage=4.0):
    if df.empty:
        pass
    else:
        last_close = df[-1:]['Close'].values[0]
        #print (last_close)

    if is_consolidating(df[:-1], percentage=percentage):
        recent_closes = df[-16:-1]

        if last_close > recent_closes['Close'].max():
            return True

    return False

for filename in os.listdir('datasets/daily'):
    df = pandas.read_csv('datasets/daily/{}'.format(filename))

    if is_consolidating(df):
        df.to_csv('export_consolidators.csv')
        print("{} is consolidating".format(filename))
        
    if is_breaking_out(df):
        df.to_csv('export_breakouts.csv')
        print("{} is breaking out".format(filename))

Answer 1

Write mode of pandas.DataFrame.to_csv() is w by default. Possible values can referred to open() .

I think you want to store each kind of dataframe to separate files by appending. You may do

for filename in os.listdir('datasets/daily'):
    df = pandas.read_csv('datasets/daily/{}'.format(filename))

    if is_consolidating(df):
        df.to_csv('export_consolidators.csv', mode='a', header=False)
        print("{} is consolidating".format(filename))
        
    if is_breaking_out(df):
        df.to_csv('export_breakouts.csv', mode='a', header=False)
        print("{} is breaking out".format(filename))

a here means append mode.

You could also store them in a list first, then pandas.concat() each list into one dataframe.

consolidating_list = []
breaking_out_list = []

for filename in os.listdir('datasets/daily'):
    df = pandas.read_csv('datasets/daily/{}'.format(filename))

    if is_consolidating(df):
        consolidating_list.append(df)
        print("{} is consolidating".format(filename))
        
    if is_breaking_out(df):
        breaking_out_list.append(df)
        print("{} is breaking out".format(filename))

combined_consolidating = pd.concat(consolidating_list).reset_index()
combined_breaking_out = pd.concat(breaking_out_list).reset_index()

combined_consolidating.to_csv('export_consolidators.csv')
combined_breaking_out.to_csv('export_breakouts.csv')

If there are many kinds of dataframe. You could use a two-dimensional list to decrease the redundant statement.