Pandas: find the minimum value in a column, write the row containing that column into a new dataframe
I have a large number of simple time series in unique CSV files. Each file contains a Date column and a Close column.
I would like to use pandas to read data for each file in a dataframe, find the minimum value in the Close column, and write both the minimum Close value and the associated Date to the new dataframe.
Ideally, this will create a new framework that contains the minimum close values and the date that minimum level occurred for all files that are escaped.
import pandas as pd
import os
symbol = "LN"
start_year = 2010
end_year = 2014
months = ["G", "J", "M", "N", "Q", "V", "Z"]
def historiclows():
df1 = pd.read_csv("%s.csv" % (file3))
df1 = df1.drop(df1.columns[[1,2,3,5,6]], axis = 1)
targetvalues = df1.loc[df1["Close"].idxmin()]
df2.append(targetvalues)
for m in months:
df2 = pd.DataFrame()
for y in range(start_year, end_year+1):
if m != "Z":
if months[months.index(m)+1] != "Z":
file1 = ("%s%s%s%s%s%s" % (symbol, m, y, symbol, months[months.index(m)+1], y))
file2 = ("%s%s%s%s%s%s" % (symbol, months[months.index(m)+1], y, symbol, months[months.index(m)+2], y))
file3 = ("%s%s" % (file1, file2))
checkfile3 = os.path.isfile("%s.csv" % file3)
if checkfile3 == True:
title = ("%s%s%s" % (m, months[months.index(m)+1], months[months.index(m)+2]))
historiclows()
print(df2)
else:
pass
else:
file1 = ("%s%s%s%s%s%s" % (symbol, m, y, symbol, months[months.index(m)+1], y))
file2 = ("%s%s%s%s%s%s" % (symbol, months[months.index(m)+1], y, symbol, str(months[0]), y+1))
file3 = ("%s%s" % (file1, file2))
checkfile3 = os.path.isfile("%s.csv" % file3)
if checkfile3 == True:
title = ("%s%s%s" % (m, months[months.index(m)+1], str(months[0])))
historiclows()
print(df2)
else:
pass
else:
file1 = ("%s%s%s%s%s%s" % (symbol, m, y, symbol, str(months[0]), y+1))
file2 = ("%s%s%s%s%s%s" % (symbol, str(months[0]), y+1, symbol, str(months[1]), y+1))
file3 = ("%s%s" % (file1, file2))
checkfile3 = os.path.isfile("%s.csv" % file3)
if checkfile3 == True:
title = ("%s%s%s" % (m, str(months[0]), str(months[1])))
historiclows()
print(df2)
else:
pass
print ("!!! PROCESS COMPLETE !!!")
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1 answer
You can simply do:
>> orig_df
Close
2015-01-01 4
2015-02-01 1
2015-03-01 3
2015-03-01 1
new_df = orig_df[orig_df['Close'] == min(orig_df['Close'])]
>> new_df
Close
2015-02-01 1
2015-03-01 1
Then, if you want the minimum to appear once in the new dataframe, you can use drop_duplicates
:
new_df.drop_duplicates(subset=['Close'], inplace=True)
>> Close
2015-02-01 1
If you want to get the last date and not the first date, do
new_df.drop_duplicates(subset=['Close'], inplace=True, take_last=True)
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