Write text to csv file python

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Write text to csv file python

Your comment on this question: Your comment on this answer: Your comment on this answer: Your comment on this answer: Your comment on this answer: Related Questions In Python Teaching: 60 min Exercises: 30 min Questions How can I read in data that is stored in a file or write data out to a file? Objectives Be able to open a file

and read in the data stored in that file Understand the difference between the file name, the opened file object, and the data read in from the file Be able to write output to a text file with simple formatting Being able to open and read in files allows us to work with larger data sets, where it wouldn¡¯t be possible to type in each and every value

and store them one-at-a-time as variables. Writing files allows us to process our data and then save the output to a file so we can look at it later. Right now, we will practice working with a comma-delimited text file (.csv) that contains several columns of data. However, what you learn in this lesson can be applied to any general text file. In

the next lesson, you will learn another way to read and process .csv data. Paths to files In order to open a file, we need to tell Python exactly where the file is located, relative to where Python is currently working (the working directory). In Spyder, we can do this by setting our current working directory to the folder where the file is located.

Or, when we provide the file name, we can give a complete path to the file. We will work with the practice file Plates_output_simple.csv. Locate the file Plates_output_simple.csv in the directory home/Desktop/workshops/bash-git-python. Copy the file to your working directory, home/Desktop/workshops/YourName. Make sure that your

working directory is also set to the folder home/Desktop/workshops/YourName. As you are working, make sure that you save your file opening script(s) to this directory. The File Setup Let¡¯s open and examine the structure of the file Plates_output_simple.csv. If you open the file in a text editor, you will see that the file contains several lines

of text. However, this is fairly difficult to read. If you open the file in a spreadsheet program such as LibreOfficeCalc or Excel, you can see that the file is organized into columns, with each column separated by the commas in the image above (hence the file extension .csv, which stands for comma-separated values). The file contains one

header row, followed by eight rows of data. Each row represents a single plate image. If we look at the column headings, we can see that we have collected data for each plate: The name of the image from which the data was collected The plate number (there were 4 plates, with each plate imaged at two different time points) The growth

condition (either control or experimental) The observation timepoint (either 24 or 48 hours) Colony count for the plate The average colony size for the plate The percentage of the plate covered by bacterial colonies We will read in this data file and then work to analyze the data. Opening and reading files is a three-step process We will

open and read the file in three steps. We will create a variable to hold the name of the file that we want to open. We will call a open to open the file. We will call a function to actually read the data in the file and store it in a variable so that we can process it. And then, there¡¯s one more step to do! When we are done, we should remember to

close the file! You can think of these three steps as being similar to checking out a book from the library. First, you have to go to the catalog or database to find out which book you need (the filename). Then, you have to go and get it off the shelf and open the book up (the open function). Finally, to gain any information from the book, you

have to read the words (the read function)! Here is an example of opening, reading, and closing a file. #Create a variable for the file name filename = 'Plates_output_simple.csv' #This is simply a string of text #Open the file infile = open(filename, 'r') # 'r' says we are opening the file to read, infile is the opened file object that we will read

from #Store the data from the file in a variable data = infile.read() #Print the data in the file print(data) #close the file infile.close() Once we have read the data in the file into our variable data, we can treat it like any other variable in our code. It is a good idea to develop some consistent habits about the way you open and read files. Using

the same (or similar!) variable names each time will make it easier for you to keep track of which variable is the name of the file, which variable is the opened file object, and which variable contains the read-in data. In these examples, we will use filename for the text string containing the file name, infile for the open file object from which

we can read in data, and data for the variable holding the contents of the file. Commands for reading in files There are a variety of commands that allow us to read in data from files. infile.read() will read in the entire file as a single string of text. infile.readline() will read in one line at a time (each time you call this command, it reads in the

next line). infile.readlines() will read all of the lines into a list, where each line of the file is an item in the list. Mixing these commands can have some unexpected results. #Create a variable for the file name filename = 'Plates_output_simple.csv' #Open the file infile = open(filename, 'r') #Print the first two lines of the file print(infile.readline())

print(infile.readline()) #call infile.read() print(infile.read()) #close the file infile.close() Notice that the infile.read()command started at the third line of the file, where the first two infile.readline() commands left off. Think of it like this: when the file is opened, a pointer is placed at the top left corner of the file at the beginning of the first line. Any

time a read function is called, the cursor or pointer advances from where it already is. The first infile.readline() started at the beginning of the file and advanced to the end of the first line. Now, the pointer is positioned at the beginning of the second line. The second infile.readline() advanced to the end of the second line of the file, and left

the pointer positioned at the beginning of the third line. infile.read() began from this position, and advanced through to the end of the file. In general, if you want to switch between the different kinds of read commands, you should close the file and then open it again to start over. Reading all of the lines of a file into a list infile.readlines() will

read all of the lines into a list, where each line of the file is an item in the list. This is extremely useful, because once we have read the file in this way, we can loop through each line of the file and process it. This approach works well on data files where the data is organized into columns similar to a spreadsheet, because it is likely that we

will want to handle each line in the same way. The example below demonstrates this approach: #Create a variable for the file name filename = "Plates_output_simple.csv" #Open the file infile = open(filename, 'r') lines = infile.readlines() for line in lines: #lines is a list with each item representing a line of the file if 'control' in line: print(line)

#print lines for control condition infile.close() #close the file when you're done! Using .split() to separate ¡°columns¡± Since our data is in a .csv file, we can use the split command to separate each line of the file into a list. This can be useful if we want to access specific columns of the file. #Create a variable for the file name filename =

"Plates_output_simple.csv" #Open the file infile = open(filename, 'r') lines = infile.readlines() for line in lines: sline = line.split(',') # separates line into a list of items. ',' tells it to split the lines at the commas print(sline) #each line is now a list infile.close() #Always close the file! At first glance, the variable name sline in the example above may

not make much sense. In fact, we chose it to be an abbreviation for ¡°split line¡±, which exactly describes the contents of the variable. You don¡¯t have to use this naming convention if you don¡¯t want to, but you should work to use consistent variable names across your code for common operations like this. It will make it much easier to open

an old script and quickly understand exactly what it is doing. When we called the readlines() command in the previous code, Python reads in the contents of the file as a string. If we want our code to recognize something in the file as a number, we need to tell it this! For example, float('5.0') will tell Python to treat the text string ¡®5.0¡¯ as the

number 5.0. int(sline[4]) will tell our code to treat the text string stored in the 5th position of the list sline as an integer (non-decimal) number. For each line in the file, the ColonyCount is stored in the 5th column (index 4 with our 0-based counting). Modify the code above to print the line only if the ColonyCount is greater than 30. #Create a

variable for the file name filename = 'Plates_output_simple.csv' ##Open the file infile = open(filename, 'r') lines = infile.readlines() for line in lines[1:]: #skip the first line, which is the header sline = line.split(',') # separates line into a list of items. ',' tells it to split the lines at the commas colonyCount = int(sline[4]) #store the colony count for

the line as an integer if colonyCount > 30: print(sline) #close the file infile.close() Writing data out to a file Often, we will want to write data to a new file. This is especially useful if we have done a lot of computations or data processing and we want to be able to save it and come back to it later. Writing a file is the same multi-step process

Just like reading a file, we will open and write the file in multiple steps. Create a variable to hold the name of the file that we want to open. Often, this will be a new file that doesn¡¯t yet exist. Call a function to open the file. This time, we will specify that we are opening the file to write into it! Write the data into the file. This requires some

careful attention to formatting. When we are done, we should remember to close the file! The code below gives an example of writing to a file: filename = "output.txt" #w tells python we are opening the file to write into it outfile = open(filename, 'w') outfile.write("This is the first line of the file") outfile.write("This is the second line of the file")

outfile.close() #Close the file when we¡¯re done! Any time you open a new file and write to it, the file will be saved in your current working directory, unless you specified a different path in the variable filename. Newline characters When you examine the file you just wrote, you will see that all of the text is on the same line! This is because

we must tell Python when to start on a new line by using the special string character ''. This newline character will tell Python exactly where to start each new line. The example below demonstrates how to use newline characters: filename = 'output_newlines.txt' #w tells python we are opening the file to write into it outfile = open(filename,

'w') outfile.write("This is the first line of the file") outfile.write("This is the second line of the file") outfile.close() #Close the file when we¡¯re done! Go open the file you just wrote and and check that the lines are spaced correctly.: You may have noticed in the last file reading example that the printed output included newline characters at the

end of each line of the file: [¡®colonies02.tif¡¯, ¡®2¡¯, ¡®exp¡¯, ¡®24¡¯, ¡®84¡¯, ¡®3.2¡¯, ¡®22¡¯] [¡®colonies03.tif¡¯, ¡®3¡¯, ¡®exp¡¯, ¡®24¡¯, ¡®792¡¯, ¡®3¡¯, ¡®78¡¯] [¡®colonies06.tif¡¯, ¡®2¡¯, ¡®exp¡¯, ¡®48¡¯, ¡®85¡¯, ¡®5.2¡¯, ¡®46¡¯] We can get rid of these newlines by using the .strip() function, which will get rid of newline characters: #Create a variable for the file name filename = 'Plates_output_simple.csv'

##Open the file infile = open(filename, 'r') lines = infile.readlines() for line in lines[1:]: #skip the first line, which is the header sline = line.strip() #get rid of trailing newline characters at the end of the line sline = sline.split(',') # separates line into a list of items. ',' tells it to split the lines at the commas colonyCount = int(sline[4]) #store the

colony count for the line as an integer if colonyCount > 30: print(sline) #close the file infile.close() Writing numbers to files Just like Python automatically reads files in as strings, the write()function expects to only write strings. If we want to write numbers to a file, we will need to ¡°cast¡± them as strings using the function str(). The code below

shows an example of this: numbers = range(0, 10) filename = "output_numbers.txt" #w tells python we are opening the file to write into it outfile = open(filename, 'w') for number in numbers: outfile.write(str(number)) outfile.close() #Close the file when we¡¯re done! Go open and examine the file you just wrote. You will see that all of the

numbers are written on the same line. Modify the code to write each number on its own line. numbers = range(0, 10) #Create the range of numbers filename = "output_numbers.txt" #provide the file name #open the file in 'write' mode outfile = open(filename, 'w') for number in numbers: outfile.write(str(number) + '') outfile.close() #Close the

file when we¡¯re done! The file you just wrote should be saved in your Working Directory. Open the file and check that the output is correctly formatted with one number on each line. When we have opened files to read or write data, we have used the function parameter 'r' or 'w' to specify which ¡°way¡± to open the file. 'r' indicates we are

opening the file to read data from it. 'w' indicates we are opening the file to write data into it. Be very, very careful when opening an existing file in ¡®w¡¯ mode. 'w' will over-write any data that is already in the file! The overwritten data will be lost! If you want to add on to what is already in the file (instead of erasing and over-writing it), you can

open the file in append mode by using the 'a' parameter instead. Read in the data from the file Plates_output_simple.csv that we have been working with. Write a new csv-formatted file that contains only the rows for control plates. You will need to do the following steps: Open the file. Use .readlines() to create a list of lines in the file. Then

close the file! Open a file to write your output into. Write the header line of the output file. Use a for loop to allow you to loop through each line in the list of lines from the input file. For each line, check if the growth condition was experimental or control. For the control lines, write the line of data to the output file. Close the output file when

you¡¯re done! Here¡¯s one way to do it: #Create a variable for the file name filename = 'Plates_output_simple.csv' ##Open the file infile = open(filename, 'r') lines = infile.readlines() #We will process the lines of the file later #close the input file infile.close() #Create the file we will write to filename = 'ControlPlatesData.txt' outfile =

open(filename, 'w') outfile.write(lines[0]) #This will write the header line of the file for line in lines[1:]: #skip the first line, which is the header sline = line.split(',') # separates line into a list of items. ',' tells it to split the lines at the commas condition = sline[2] #store the condition for the line as a string if condition == "control": outfile.write(line)

#The variable line is already formatted correctly! outfile.close() #Close the file when we¡¯re done! Open and read in the data from Plates_output_simple.csv. Write a new csv-formatted file that contains only the rows for the control condition and includes only the columns for Time, colonyCount, avgColonySize, and percentColonyArea. Hint:

you can use the .join() function to join a list of items into a string. names = ['Erin', 'Mark', 'Tessa'] nameString = ', '.join(names) #the ', ' tells Python to join the list with each item separated by a comma + space print(nameString) ¡®Erin, Mark, Tessa¡¯ #Create a variable for the input file name filename = 'Plates_output_simple.csv' ##Open the

file infile = open(filename, 'r') lines = infile.readlines() #We will process the lines of the file later #close the file infile.close() # Create the file we will write to filename = 'ControlPlatesData_Reduced.txt' outfile = open(filename, 'w') #Write the header line headerList = lines[0].split(',')[3:] #This will return the list of column headers from 'time' on

headerString = ','.join(headerList) #join the items in the list with commas outfile.write(headerString) #There is already a newline at the end, so no need to add one #Write the remaining lines for line in lines[1:]: #skip the first line, which is the header sline = line.split(',') # separates line into a list of items. ',' tells it to split the lines at the

commas condition = sline[2] #store the colony count for the line as an integer if condition == "control": dataList = sline[3:] dataString = ','.join(dataList) outfile.write(dataString) #The variable line is already formatted correctly! outfile.close() #Close the file when we¡¯re done! Opening and reading a file is a multistep process: Defining the

filename, opening the file, and reading the data Data stored in files can be read in using a variety of commands Writing data to a file requires attention to data types and formatting that isn¡¯t necessary with a print() statement Page 2 Teaching: 60 min Exercises: 30 min Questions How can I analyze a data table? Objectives Be able to load a

.csv formatted data file using numpy Understand how to access specific cells, rows, or columns in the data file Perform simple mathematical operations on the data or subsets of the data In the lesson on Files we saw one way to deal with a .csv-formatted data file. However, Python has some built in libraries that allow us to handle a data

table much more easily. Libraries A library is a set of functions that you can import and use in your code. Usually, libraries contain functions that not all users will regularly need. Thus, there are many libraries available that are designed for specialized tasks. Tomorrow, you will begin working with the OpenCV library, which contains many

functions that are useful for image processing. Today, we are going to use the library numpy, which contains many tools for analyzing data stored in a table (like a csv file!). We can import numpy and all of its functions by including the command import numpy as np at the beginning of our code. This will let us call functions in the numpy

library as np.(). We will work with the practice file Titration_color_data_simple.csv. Locate the file Titration_color_data_simple in the directory home/Desktop/workshops/bash-git-python. Copy the file to your working directory, home/Desktop/workshops/YourName. Make sure that your working directory is also set to the folder

home/Desktop/workshops/YourName. As you are working, make sure that you save your file opening script(s) to this directory. The File Setup Let¡¯s open and examine the structure of the file Titration_color_data_simple.csv. If you open the file, you will see that it contains a header row, followed by three rows of data. Each row represents

a single color channel (either red, green, or blue) measured over time. Each column represents a different frame of the image. The goal of the file is to track the color of the titration solution over time, to identify the point when the reaction is finished. We will read in this data file and then work to analyze the data. The code below will open

and load the datafile. import numpy as np data = np.loadtxt(fname = 'Titration_color_data_simple.csv', delimiter =',', skiprows = 1, usecols=(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)) print(data) Let¡¯s dig into this piece of code a little more: The first half of the first line, import numpy, is the statement that tells Python to load the functions in the

numpy library so that they will be available to use in your code. The second half of that line, as np, creates a shorthand name for the numpy library. Now, anytime you want to use a numpy function, you only have to type np. instead of numpy.. The expression np.loadtxt(...) is a function call that asks Python to run the function named loadtxt

which is part of the numpy library (which we asked Python to abbeviate as np). This dotted notation is used everywhere in Python to refer to the component parts of things as ponent (in this case, libraryName.functionName(), since the function is a part of the library). We supplied np.loadtxt with four parameters: fname is the

name of the file we want to read delimiter is the text that separates values on a line (in this case a comma). These both need to be character strings, so we put them in quotes. skiprows = 1 tells python to skip 1 row of the file that contains the column names (numpy can only handle numbers, not a mix of text strings and numbers).

usecols=(¡­) tells Python to only read in these columns. Note that if we are reading in a csv file that contains only numbers (no column names or row names), we can omit the last two arguments skiprows and usecols Also notice that np.loadtxt() handled all of the file opening and parsing for us in a single command! Locating specific

values within the numpy array The command data.shape will return a tuple that tells us the number of rows and columns in our numpy data array: import numpy as np data = np.loadtxt(fname = 'Titration_color_data_simple.csv', delimiter =',', skiprows = 1, usecols=(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)) print(data.shape) This useful

command can be used to quickly check that our data was read in correctly. The output (3,15) tells us that we have an array of data with 3 rows and 15 columns. We can look up specific values in the array using indexing. For example, we can look up the very first element by using the command print(data[0,0]) which returns 190.0 What

does print(data[2,0]) return? What does it tell you about the indexing (which number is the row and which number is the column? print(data[2,0]) prints out 175.0 which is the first value (index 0) in the third row (index 2). In general, numpy arrays are indexed by [row, column], using zero-based indexing. How would you print out the last

(bottom right) number of the array? print(data[2,14]) prints out 172.0 which is the fifteenth and last value (index 14) in the bottom row (index 2). Note that you could also have done something like this if you did already know the number of rows and colunmns: nRows = data.shape[0] #gets the number of rows nCols = data.shape[1] #gets

the number of columns print(data[nRows-1, nCols-1]) #subtract 1 to adjust for 0-based indexing We can also use this indexing to access subsections or whole rows and columns of the array. print(data[2,:]) will return all of row 2. print(data[:,2]) will return all of column 2. print(data[0:2,1:3]) will print the first two rows of the array (0:2),

columns 1 and 2 only (1:3). Remember that the indexing is 0-based, and the ranges are up to but not inclusive, so that 1:3 gets columns 1 and 2 but not column 3. How would you print out the last two columns of the middle and bottom two rows of the array data? Your output should look like this: [[ 154. 155.] [ 171. 172.]] print(data[1:3,

13:15]) will print the values that you want. Since the array has 3 rows, the middle row is located at index 1 and the bottom row at index 2 (remember: 0-based indexing). We include 1:3 because indexing is up to, but not inclusive. Similarly, since we have 15 columns, 13:15 will return the last two columns, located at indexes 13 and 14.

Calling Functions on the numpy array Python is able to do simple arithmetic operations on numpy arrays. For example, we can divide each value in the array by 2 scaleData = data/2 print(data) print(scaleData) We can do similar operations using +, -, and * (the multiplication operator). However, we often want to do more complicated

operations, such as taking an average, or finding the minimum or maximum value. For example, the code below will find the average value of the entire dataset. mean is a something called a function. Functions are built-in operations that can do things with variables. All functions have the name of the function (in this case, np.mean)

followed by a set of parentheses (). The text inside the parentheses is called the function¡¯s argument, and it tells Python what to do the operation on. After completing the operation, the function returns a specified output (in this case, a number representing the average of the data). Functions are useful because they can condense many

lines of code into one simple command that we can call. You have already seen some functions: split() for breaking apart strings is one example; print() and open() are other examples. .shape is not a function (it lacks parentheses and an argument). Instead, it is a piece of information about the variable data that was created when we

created the numpy array. It describes the variable data, just like an adjective describes a noun. These descriptive pieces of information are called attributes. Other useful functions include the following: maxval = np.max(data) #returns the maximum value minval = np.min(data) #returns the minimum value stdev = np.std(data) #returns the

standard deviation of all values print('maximum value:', maxval) print('minimum value:', minval) print('standard deviation:', stdev) Applying functions to an axis Of course, none of the examples above makes much sense for our data, since we have different kinds of data in each column of our array. maxval = np.max(data), minval =

np.min(data), and stdev = np.std(data)each applied the function to the entire data set. What if we want to apply the function to each row or each column individually? One way is to create a variable storing just the row or column we are interested in. reds = data[0,:] #the RedIntensity row avgRed = np.mean(reds) print('The average red

intensity is: ', str(avgRed)) However, this will quickly get awkward and cumbersome if we need to do this for every column or row in a large dataset (although, we could use a for loop). A more efficient way is to apply the function over each row or each column by specifying an axis. The code below will return the maximum value of each

column and the average value of each row in data, respectively. print(np.max(data, axis=0)) #axis=0 applies the function to each column print(np.mean(data, axis=1)) #axis=1 applies the function to each row Create a new dataset called data2 that contains only the last 6 columns of the dataset data. Compute the minimum value for each

column in data2 and the average value for each row. import numpy as np data = np.loadtxt(fname = 'Titration_color_data_simple.csv', delimiter =',', skiprows = 1, usecols=(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)) data2 = data[:, -6:] #create the column subset print(np.min(data2, axis=0)) #axis=0 applies the function to each column

print(np.mean(data2, axis=1)) #axis=1 applies the function to each row Read in the Titration_color_data_simple.csv file as a numpy array. Use the numpy functions you have learned to identify the color channel that experiences the largest change. import numpy as np data = np.loadtxt(fname = 'Titration_color_data_simple.csv', delimiter

=',', skiprows = 1, usecols=(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)) #apply the min and max function to each row, then take the difference to find the change mins = np.min(data, axis=1) #axis=0 applies the function to each column maxes = np.max(data, axis=1) #axis=1 applies the function to each row difference = maxes-mins

print(difference) stdev = np.min(data, axis=1) print(stdev) [ 11. 36. 8.] [ 2.43493098 12.68945319 2.59401019] The output of this program shows that the second row, which represents that green channel, has both the largest max-min difference, and the highest standard deviation. Libraries contain specialized functions that can be

imported and used in your code The numpy library contains functions that allow you to easily read in a .csv-formatted data file and perform simple operations on rows and columns of the data Page 3 Teaching: 30 min Exercises: 30 min Questions How can I write my own functions? Objectives Know the basic syntax of a function Be able to

explain the difference between writing a function and calling a function Practice writing and calling simple functions Be able to import functions into another script Often, we will need to do the same calculations and the same set of commands over and over again on multiple files, or multiple pieces of data. Any time you are going to repeat

a set of commands, or may want to repeat them later, it makes sense to define a function. Then, every time we need to do that set of commands, we only have to call the function instead of typing each command individually. Let¡¯s start by defining a simple function to convert temperatures in Fahrenheit to Kelvin. def fahr_to_kelvin(temp):

return ((temp - 32) * (5/9)) + 273.15 The function contains the keyword def and a function name (in this case, fahr_to_kelvin). The input parameters (or arguments that must be passed to the function are listed inside of the parentheses. In the function definition, these paramaters are placeholder variables. When we actually use the function

later, we will have to supply a value for temp that the function will operate on. Finally, the function definition also contains a return statement, which tells us what value(s) function will output. Notice that typing the function doesn¡¯t do anything. In order to use the function, we have to call it, or tell Python to perform the function on an input. To

call it, we use the name of the function with the desired arguments inside the parentheses (in this case, a number representing a temperature in degrees Fahrenheit). print(fahr_to_kelvin(0)) print(fahr_to_kelvin(-324)) The value inside of the parentheses is assigned to the variable name temp in the function definition above so that the

program can perform the temperature conversion calculation and return a number representing the converted temperature. Now, if we need to redo this calculation over and over on different numbers, we can just call the function on each of those numbers instead of typing in the entire calculation and potentially making mistakes. Create a

second function that will convert temperatures from Fahrenheit to Celsius. Test your function by callling it on a few different numbers. The code below shows you how to write the function and then call it: def fahr_to_celsius(temp): return (temp - 32) * (5/9) print(fahr_to_celsius(72)) If you need some test values, try the following:

fahr_to_celsius(212) should return 100 fahr_to_celsius(32) should return 0 fahr_to_celsius(82) should return 27.778 Create a new function that will take a temperature in Fahrenheit, convert it to both Kelvin and Celsius, and then return a list of the three temperatures in this order: [original Fahrenheit, Kelvin, and Celsius]. Here is one way

to do it: def temp_converter(ftemp): ctemp = (ftemp - 32) * (5/9) ktemp = ctemp + 273.15 return [ftemp, ktemp, ctemp] print(temp_converter(72)) If you need some test values, try the following: temp_converter(212) should return [212, 373.15, 100] temp_converter(32) should return [32, 373.15, 0] temp_converter(82) should return [82,

300.928, 27.778] Importing functions Now that we have written a few functions, we can even import them and use them in other scripts! Save your current file as function_examples.py. Open a new script file in Spyder. At the top of your script, include this line: from function_examples import fahr_to_kelvin Now, we can use our

fahr_to_kelvin function in our new script! print(fahr_to_kelvin(15)) In order to import functions from another script, the script must be saved in the same folder as the script you are running. Otherwise, Python won¡¯t know where to look for the file to import the function you want. If the script with functions to be imported is in another folder,

you will have to supply the complete path to the file as part of the import statement. Open a new script. Take the temperature conversion function that we wrote above to return the original fahrenheit, Kelvin, and Celsius temperatures and modify it to import and use the functions fahr_to_kelvin and fahr_to_celsius. from function_examples

import fahr_to_kelvin from temp_conversion import fahr_to_celsius #Change temp_converion to the name of the script file with this function #Define the function def temp_converter(ftemp): ctemp = fahr_to_celsius(ftemp) ktemp = fahr_to_kelvin(ftemp) return [ftemp, ktemp, ctemp] #call the function print(temp_converter(72)) Write a

function that examines one color channel (one row) of the file Titration_color_data_simple.csv and returns the frame that has largest change in intensity from the frame before it. For example, if you had the data intensities = [140, 142, 139, 128, 129, 126] representing frames 1-6 for a single channel you should return the number 3

because the biggest difference is 139-128 = 11 between frames 3 and 4. You should be able to read in the entire dataset (either using standard read commands or as a numpy array) and then run your function on each color channel individually. Plan out all of the steps you need to accomplish before you start writing your code. Don¡¯t be

afraid to think creatively, drawing on all of the Python tools you have learned! Function statements start with the keyword def, and must include a name, parameters, and a return statement Functions must be called in order to use them Functions can be imported to be used by other scripts using the import command

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