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Format a column for excel date format from Format to Date within python script

df.to_excel(filename, index = False)
This is my current code for exporting data frame to an excel, column index 0 and 2 contain dates, but I need these columns to be formatted as an Excel date format. I have read online but I don't understand how to achieve this.
Can someone please explain
import pandas as pd
from datetime import datetime
writer = pd.ExcelWriter(testing.xlsx", datetime_format='dd/mm/yyyy')
hold.to_excel(writer, "Sheet1", index = False)
writer.close()
print("done")

add column to a dataframe in python pandas

How do i loop through my excel sheet and add each 'Adjusted Close' to a dataframe? I want to summarize all adj close and make an stock indice.
When i try with the below code the dataframe Percent_Change is empty.
xls = pd.ExcelFile('databas.xlsx')
countSheets = len(xls.sheet_names)
Percent_Change = pd.DataFrame()
x = 0
for x in range(countSheets):
data = pd.read_excel('databas.xlsx', sheet_name=x, index_col='Date')
# Calculate the percent change from day to day
Percent_Change[x] = pd.Series(data['Adj Close'].pct_change()*100, index=Percent_Change.index)
stock_index = data['Percent_Change'].cumsum()

unfortunately I do not have the data to replicate your complete example. However, there appears to be a bug in your code.
You are looping over "x" and "x" is a list of integers. You probably want to loop over the sheet names and append them to your DF. If you want to do that your code should be:
import pandas as pd
xls = pd.ExcelFile('databas.xlsx')
# pep8 unto thyself only, it is conventional to use "_" instead of camelCase or to avoid longer names if at all possible
sheets = xls.sheet_names
Percent_Change = pd.DataFrame()
# using sheet instead of x is more "pythonic"
for sheet in sheets:
data = pd.read_excel('databas.xlsx', sheet_name=sheet, index_col='Date')
# Calculate the percent change from day to day
Percent_Change[sheet] = pd.Series(data['Adj Close'].pct_change()*100, index=Percent_Change.index)
stock_index = data['Percent_Change'].cumsum()

Subtract cell in one column from the one prior in xlsx file in Python

I have an xlsx file with multiple sheets. In the sheets there is column A
with time stamps (as strings). I need to subtract the cells from the one above it to see how much time has elapsed.
ex. COLUMN A
02/23/2017 08:25:39
02/23/2017 08:55:56
02/23/2017 08:55:57
02/23/2017 08:56:12
Here is what I have so far.....Thank you in advance.
import xlrd
from datetime import datetime
def open_file(path):
# Open and read an Excel file
book = xlrd.open_workbook(path)
# get the first worksheet
first_sheet = book.sheet_by_index(0)
# read first column
column_values = first_sheet.col_values(0,0)
column_list = []
for i in column_values:
i = datetime.strptime(i, '%m/%d/%Y %H:%M:%S')
column_list.append(i)
print(column_list[1] - column_list[0])
if __name__ == "__main__":
path = '02-23-2017.xlsx'
open_file(path)

You may want to check out pandas. It handles calculations like this quickly.
import pandas as pd
# create a dictionary of data frames, one for each sheet
df_dict = pd.read_excel('C:/path/to/file.xlsx', sheets=None, header=None)
# iterate over each data frame
for df_key in df_dict:
# pull the time data from the first columns
t = pd.to_datetime(df_dict[df_key].iloc[:,0])
# calculate the time difference using .diff(1), fillna makes the first cell 0
dt = t.diff(1).fillna(0)
# assign the difference to a new columns in the data frame
df_dict[df_key]['time_delta'] = dt
# create a writer to make a new excel file
writer = pd.ExcelWriter('C:/path/to/new_file.xlsx')
# write each sheet to file
for name, df in df_dict.items():
df.to_excel(writer, 'sheet{}'.format(name))
writer.save()

Based on your existing code, you could do the following:
import xlrd
from datetime import datetime
def open_file(path):
# Open and read an Excel file
book = xlrd.open_workbook(path)
# Open each sheet
for sheet in book.sheet_names():
current_sheet = book.sheet_by_name(sheet)
# Read first column and convert to datetime objects
column_values = [datetime.strptime(i, '%m/%d/%Y %H:%M:%S') for i in current_sheet.col_values(0, 0)]
# Create a list of timedelta differences
cur = column_values[0]
differences = []
for i in column_values[1:]:
differences.append(i - cur)
cur = i
print(sheet)
for d in differences:
print(" {}".format(d))
if __name__ == "__main__":
path = '02-23-2017.xlsx'
open_file(path)
Assuming each of the sheets has the same format, this would give you something like:
Sheet1
0:30:17
0:00:01
0:00:15
Sheet2
0:30:17
0:00:01
0:00:15
Sheet3
0:30:17
0:00:01
0:00:15

Saving to_csv read just the columns, not the rows

I'm stuck with reading all the rows of a csv file and save into a csv files (I'm using pandas 0.17.1).
I've a list of tickers inserted into a csv file: they are inserted into each column, like this:
Column A: AAPL / Column B:TSLA / Column C: EXPD... and so on.
Now, I've to add 3000 new tickers to this list, and so I change the orientation of the csv, bringing every ticker into each row of the first column, like this:
Column A
AAPL
TSLA
EXPD
...and so on.
The issue is: when I save the document into a csv file, it read only the first row, and nothing else.
In my example, if i have on the first row "AAPL", I will obtain a csv file that has only the data from AAPL.
This is my code:
symbols_list = pd.read_csv('/home/andrea/htrade/python/titoli_rows.csv')
symbols = []
for ticker in symbols_list:
r = DataReader(ticker, "yahoo",
start=datetime.datetime.now() - BDay(20),
end=datetime.datetime.now())
# add a symbol column
r['Symbol'] = ticker
symbols.append(r)
# concatenate all the dfs
df = pd.concat(symbols)
#define cell with the columns that i need
cell = df[['Symbol', 'Open', 'High', 'Low', 'Adj Close', 'Volume']]
cell.reset_index().sort_values(['Symbol', 'Date'], ascending=[1, 0]).set_index('Symbol').to_csv('/home/andrea/Dropbox/HT/stock20.csv', date_format='%d/%m/%Y')
Why if I paste a ticker in each column the csv contain all the data of every ticker, but if I paste a ticker in each row, it will read just the first row?
I already tried to see if the "read_csv" function was reading correctly the csv, and he is, so I don't understand why he's not elaborating them all.

I just ran the below and with a short list of symbols imported via read_csv it seemed to work fine:
from datetime import datetime
import pandas.io.data as web
from pandas.tseries.offsets import BDay
df = pd.read_csv(path_to_file).loc[:, ['symbols']].dropna().squeeze()
symbols = []
for ticker in df.tolist():
r = web.DataReader(ticker, "yahoo",
start= datetime.now() - BDay(20),
end= datetime.now())
r['Symbol'] = ticker
symbols.append(r)
df = pd.concat(symbols).drop('Close', axis=1)
cell= df[['Symbol','Open','High','Low','Adj Close','Volume']]
cell.reset_index().sort_values(['Symbol', 'Date'], ascending=[1,0]).set_index('Symbol').to_csv(path_to_file, date_format='%d/%m/%Y')

How to write data to excel using python for stock data being pulled from yahoo

I have the following code which helps me to pull daily data for a number of stocks I have stored in a worksheet. What I was hoping to accomplish was to have the daily data returned and stored in another worksheet.
I am struggling to write a code which accomplishes this task. Currently I am able to pull the data for each of the individual stocks, though I have no way of storing this information. Any help will be appreciated. For the sake of testing I only tried to store Open and Close, ideally I would like all the parameters from yahoo finance to be stored.
import numpy as np
import pandas as pd
import xlsxwriter
df=pd.read_csv('Stock Companies Modified.csv', sep=',',header=True)
df.columns = ['StockSymbol', 'CompanyName', 'ClosingPrice', 'MarketCap', 'IPOYear', 'Sector', 'Industry']
workbook = xlsxwriter.Workbook('New Workbook.xlsx')
worksheet = workbook.add_worksheet()
df = df.convert_objects(convert_numeric=True)
df.dtypes
from pandas.io.data import DataReader
from datetime import datetime
for x in df.StockSymbol:
if len(x)<=4:
ClosingPrice = DataReader(x, 'yahoo', datetime(2015,1,1), datetime(2015,7,1))
row = 0
col = 0
#This is the area where I am getting an error, and to be honest I dont know how to do it correctly
for Open, Close in (ClosingPrice):
worksheet.write(row, col, (ClosingPrice['Open']))
worksheet.write(row,col+1,(ClosingPrice['Close']))
row+=1
workbook.close()
print x
else:
print("This is not working")

I've yet to find a clean way to append data to sheets with xlsxwriter, so typically I create a temporary dataframe with all of the values, as well as current sheet if existing - then overwrite. I would definitely prefer if we could append to sheets as you attempted but it doesn't seem possible.
import pandas as pd
from pandas.io.data import DataReader
from datetime import datetime
symbols = ['GOOG','AAPL']
try:
df = pd.read_excel('NewFile.xlsx')
except:
df = pd.DataFrame()
for symbol in symbols:
ClosingPrice = DataReader(symbol, 'yahoo', datetime(2015,1,1), datetime(2015,9,1))
ClosingPrice = ClosingPrice.reset_index()
ClosingPrice['Symbol'] = symbol
df = df.append(ClosingPrice)
writer = pd.ExcelWriter('NewFile.xlsx', engine='xlsxwriter')
df.to_excel(writer,sheet_name='Sheet1',index=False)
writer.save()
If you were later appending to this same file, it would be ok:
df = pd.read_excel('NewFile.xlsx')
symbols = ['G']
for symbol in symbols:
ClosingPrice = DataReader(symbol, 'yahoo', datetime(2015,1,1), datetime(2015,9,1))
ClosingPrice = ClosingPrice.reset_index()
ClosingPrice['Symbol'] = symbol
df = df.append(ClosingPrice)
writer = pd.ExcelWriter('NewFile.xlsx', engine='xlsxwriter')
df.to_excel(writer,sheet_name='Sheet1',index=False)
writer.save()

What is the error you are getting? Have you tried Pandas dataframe.to_excel?
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html