SyntaxError: недействительный синтаксис Python 3.5 при попытке печати в python, даже с простой печатью («Hello World»)

Question

Я только что закончил удалять кучу файлов с моего рабочего стола и других misc. папки. Я только что открыл свой проект, над которым я работал, и теперь он не будет печатать, даже с простой линией «Hello World» 144.

Я открыл новый проект python и только что напечатал («привет» мир "), и он отлично работает, поэтому я попытался выполнить мой код на том же листе и сохранить его как другое имя, и я получаю ту же ошибку.

Это код, который я использую ниже; печать полностью снизу.

from pandas_datareader import data as dreader 
import pandas as pd 
from datetime import datetime 
import dateutil.parser 
from tkinter import * 


# Sets the max rows that can be displayed 
# when the program is executed 
pd.options.display.max_rows = 200 



# df is the name of the dataframe, it is 
# reading the csv file containing data loaded 
# from yahoo finance(Date,Open,High,Low,Close 
# volume,adj close,)the name of the ticker 
# is placed before _data.csv i.e. the ticker aapl 
# would have a csv file named aapl_data.csv. 
df = pd.read_csv("cde_data.csv") 


# resets the index back to the pandas default 
# i.e. index starts at 0 for the first row and 
# 1 for the second and continues by one till the 
# end of the data in the above csv file. 
df.reset_index() 



# the following code will allow for filtering of the datafram 
# based on the year, day of week (dow), and month. It then gets 
# applied to the dataframe and then can be used to sort data i.e 
# print(df[(df.year == 2015) & (df.month == 5) & (df.dow == 4)]) 
# which will give you all the days in the month of May(df.month == 5), 
# that fall on a Thursday(df.dow == 4), in the year 2015 
# (df.year == 2015) 
# 
#  Month   Dow      Year 
# January = 1 Monday = 1 The year will be dispaly in a four 
# February = 2 Tuesday = 2 digit format i.e. 2015 
# March  = 3 Wednesday = 3 
# April  = 4 Thursday = 4 
# May  = 5 Friday = 5 
# June  = 6 
# July  = 7 
# August  = 8 
# September = 9 
# October = 10 
# November = 11 
# December = 12 
def year(x): 
    return(x.year) 
def dow(x): 
    return(x.isoweekday()) 
def month(x): 
    return(x.month) 
df.Date   = df.Date.apply(dateutil.parser.parse) 
df['year']   = df.Date.apply(year) 
df['dow']   = df.Date.apply(dow) 
df['month']  = df.Date.apply(month) 


# The code below has a total of five sections all labeled by number. 
# They are #1, #2, #3, #4, #5. Number one adds new columns to the df 
# and populates them with data, number two filters out all the days 
# that the market went down or flat for the day, number three filters 
# out all of the days that the market went up or flat, number four 
# filters all of the days that the market went up or down, and 
# number five drops the excess columns and concats steps #2, #3, & #4. 


# 1 
# there are five columns that are being added, up_down, up, down, 
# flat, and %chg. up, down, and flat are temporary and will be 
# deleted later on the other two up_down, and %chg will be permeant. 
# The up_down column is derived from taking the 'close' column minus the 
# 'open'column, this tells you how much the stock has moved for the day. 
# The 'up' column is temporary and has a value of 'up' for all the rows 
# of the DataFrame df. The 'down' column is temporary and has a value of 
# 'down' for all the rows of the DataFrame df. The 'down' column is 
# temporary and has a value of 'flat' for all the rows of the DataFrame 
# df. The '%chg' column is calculated by taking the results of the 
# 'up_down' divided by the 'close' column, and then times 100, which 
# turns it into a percentage show what percent the stock moved up or 
# down for the day. All of the columns added below are added to the 
# DataFrame called df, which contains a a csv file(see code lines 14-20 
# for information on the csv file contained in the DataFrame df). 
df['up_down']  = df['Close'] - df['Open'] 
df['up']   = 'up' 
df['down']   = 'down' 
df['flat']   = 'flat' 
df['%chg']   = ((df['up_down']/df['Close'])*100)  


# 2 
# df column[up_down] is first filtered on the greater than zero 
# criteria from the year 1984 on up and then is turned into df2. 
# If the up_down column is greater than zero than this means that 
# the stock went up. Next df3 is set = to df2['up'], df3 now holds 
# just the days where the asset went up 
df2= (df[(df.year > 1984) & (df.up_down > 0)]) 
df3 = df2['up'] 



# 3 
# df column[up_down] is first filtered on the less than zero 
# criteria from the year 1984 on up and then is turned into df4. 
# If the up_down column is less than zero than this means that 
# the stock went Down. Next df5 is set = to df4['down'], df5 now holds 
# just the days where the asset went down 
df4= (df[(df.year > 1984) & (df.up_down < 0)]) 
df5 = df4['down'] 



# 4 
# df column[up_down] is first filtered on the equal to zero 
# criteria from the year 1984 on up and then is turned into df6. 
# If the up_down column is equal to zero than this means that 
# the stock did not move. Next df7 is set = to df6['flat'],df5 
# now holds just the days where the asset did not move at all 
df6= (df[(df.year > 1984) & (df.up_down == 0)]) 
df7 = df6['flat'] 



# 5 
# The code below starts by droping the columns 'up', 'down', and 'flat'. 
# These were temporary and were used to help filter data in the above 
# code in sections two, three, and four. Finally we concat the 
# DataFrames df, df3, df5, and df7. We now have new 'up', 'down' and 
# 'flat' columns that only display up, down, or flat when the criteria 
# is true. 
df = df.drop(['up'], axis = 1) 
df = df.drop(['down'], axis = 1) 
df = df.drop(['flat'], axis = 1) 
df = pd.concat([df,df3,df5,df7],axis =1, join_axes=[df.index]) 


df['openchgprevday'] = ((df['Open']-1)-(df['Open']) 

print("Hello World") 

Это сообщение об ошибке

File "columnadder.py", line 144 
    print("Hello World") 
     ^
SyntaxError: invalid syntax 
Press any key to continue . . . 

Answer 1

Существует дополнительная скобка здесь

df['openchgprevday'] = ((df['Open']-1)-(df['Open']) 

print("Hello World") 

Чтобы исправить это, не

df['openchgprevday'] = (df['Open']-1)-(df['Open']) 

print("Hello World")