Earlier is showed you how to use the Python CSV library to read and write to CSV files. While CSV does work, and I still use elements of it occasionally, you will find working with Pandas to be so much easier.
Practice Files Excel: Linear Regression Example File 1
Let’s start with our CSV file. Unzip it and place it in a folder where you will be able to find it.
If you want to make your life a little easier, when you open your Jupyter Notebook, type pwd
This will give you your present working directory. If you place your csv file in this directory, all you have to do is call it by name.
I am not going to do that however. I want to show you how to work with directories.
Get Full Directory Path
For Windows Users, if you hold down the Shift key while right clicking on your file, you will see an option that says: Copy as Path
Read the CSV file
First import pandas as pd
Then assign a variable = pd.read_csv(file name) – paste the full path of your CSV file here.
variable.head() = the first 5 rows from your data frame.
Write CSV file
Okay, let’s write a CSV file. First, let’s add some rows to current dataframe.
We will do this be first creating a new dataframe with 3 rows of data.
Now we will use the .append() function to append this new dataframe to end of our existing dataframe.
The ignore_index = True argument tells the new rows to continue using the index of the main dataframe. Otherwise, the new rows would start counting at 0 again. If you want to try it, just leave out the argument: HgtWgt_df.append(df2)
Now, we just use the df.to_csv() command to write our appended dataframe to a new CSV file
And there is our new file
Read Excel File
I’d love to be able to wow you with how complicated reading an Excel file is, but the difference between the Excel file reading and CSV is one word – excel.
The only caveat is if your Excel file has multiple sheets. By default pd.read_excel() goes to sheet 1. If you want it to read sheet 4 instead, you would add: pd.read_excel(filename, sheetname= 4)
Write to Excel File
If you haven’t already, please check out my earlier CSV lesson: Python: Working with CSV Files
This will really give you an appreciation for how powerful and time saving pandas really is.
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