How do you merge two CSV files into one CSV file using a Python script?
Python has a standard csv library for CSV File Reading and Writing s them. The objects wrap open file-like italic objects . Use two italic CSV Reader objects s#reader-objects and one italic CSV Writer object s#writer-objects . The CSV files do not even have to have the same delimiters italic or quote characters italic . You may have to change the order of one CSV file row s to make the column orders match .
In Python 3, how do I merge thousands of CSV files, remove records with blanks, and write to one CSV efficiently without consuming huge memory?
Disclaimer I don do python not on a regular basis so this is more of an overall approach. You don need to read all files at once into memory. You should probably read the file in blocks or rows streaming it instesd of reading the entire file into memory. It depends on how you mean by merge. If you expect duplicate rows maybe you could process each CSV and store md5 hashes of each row to check if it is a duplicate. Handling hash collisions might be a bit tricky requiring you to store the filename and row number so you can reload the old file and skip to the row to do an exact check. Still uses memory for each row but considerably less. Another idea would be to load the CSVs into a database and perform aggregation there since thats what your basically doing at this point. It will do the indexing for you. Just add some identifier for the original file source maybe another table with a foreign key if that information is needed.
How can I fix 'extract the same column from two CSV files, and merge it' in Python?
The simple way is to read them both into memory and go line by line using split. Then use the correct index for each file to extract the column you want. You can be more robust by using the Python library for CSV manipulation but that takes research.
What are the best Python scripts you've ever written?
I am aputer engineer with 15 years of experience. I have created multiple python scripts (similar to many scripts described already ) for daily usage tasks. However my best python script would be facebook automation. The setup includes a selenium driver on firefox. The script is triggered once every 6 hours on a dedicatedputer. The scripts opens web browser and logs in with my account. Some of thing it can do are listed Parse my full friend list and create an xml with all relevant details. (This is important as later steps take action only on feeds from people in this created xml.) Scroll the feedpage and take actions on individual feeds. By default it will like any profile pic cover pic change. If other people congratulate my friend it can parse thement like the feed andment congratulation message. I am anonymous because most likely it against facebook policies to use this kind of scripts for daily interaction. EDIT 1 This edit section is for people who are interested in knowing how the whole script works. I will try to keep it minimal so that it doesn be too technical. The script has 3 main work areas Navigation Navigate to a webpage scroll the page etc. Action Take some action on specific element based on info collected. ordered-list Navigation Selenium driver gives the direct capability to launch a browser navigate to a scroll down etc. Hence this part is pretty much straight forward. Info collection This is one of the most hard parts. On firefox you can right click any element and inspect it . Inspect Element gives details of what the html code for an element looks like Here is a snapshot of what firefox shows when i inspect a friend name in my friends list. The class of div element is very important. I now know that whenever i will parse an element of this class it will have the details of my friend (name etc ) I first statically find these elements manually and then hardcode them in my script. I can now parse necessary elements and collect the information present in those via selenium. Selenium gives the api to extract each information of an element. For e.g. I can extract the href in above picture and i can save the of my friend. This example also covers first point of my script of how i created xml of all my friends. I need to parse my friends list only once and save it for future use until i add a friend. In a similar way we can parsements count events etc . Action Once we have collected the information we can apply our own programming logic to that information. For e.g if someone hasmented Nice picture we can post a similarment. Selenium provides the api to click on element in a area etc. So for like we simply click on Like element with that specific class. That all folks.
How do I read multiple CSV files from multiple directories using Python?
Python provides a Platform independent solution for this. You can either use glob or os modules to do that. import pandas as pd code import os code df_list = code for file in (your_directory) code df = (file) code (df) code final_df = (df for df in df_list) code ('' index=False) code Pandas module is not by default. You need to install it (using pip). Here we are reading all the csv files in the your_directory and reading them into pandas dataframes and appending it to an empty list. Once we have read all the pandas dataframes into a list we can append all of those and finally we can merge them into a single csv file (if you want). This one I have mentioned for a single directory. In the same way you can do it for multiple directories. Please let me know if you need something else in specific.
What are the benefits of a Python's pandas over Microsoft Excel for data analysis?
I don't think its a choice of Python & Panda or Excel. Rather I view them asplimentary. I wouldnt use Panda to browse data (but you could) and I wouldn't use Excel as a tool to clean up data or automate tasks (but you could). I'd use the right tool at the right time for the job. Panda has a lot of power but at a high level the module is really good at two thingsn1) Munging Data Sets helping you clean up and put data together into a format that is easy to use excel friendly and analyze. n2) Automating the clean up of data sets (missing data incongruent dates in seriesetc). Excel is simply not good at these things. Even if you are a keyboard jockey it can take hours and hours to clean up and get even the smallest data sets to the point where you can do things like pivot tables etc (think lots of selecting cutting and pasting). To give a real world example I use ad networks to monetize remnant inventory on my mobile apps. I use probably 1-15 ad networks (different apps countries etc) and each ad network generates a csv file in a slightly different format. If I were to download each of these reports by hand each day andbine them into Excel I would never have any time to actually analyze the results (not to mention the fact that this approach is fraught with the potential to create errors). As result I use Python and Pandas to take all my files clean andbine them and dump them into an Excel workbook. THEN I use Excel to browse think about and make decisions about the data. On the other hand lets say I want to do a quick ad hoc analysis and I have a fairly neat clean and reasonably sized (1s or 1s of lines) data set (e.g. stock data) I'm probably not going to write a python script to analyze it in the early stages. Rather Im just going to pull it into Excel maybe put it into a pivot table and take a look at it and noodle on it some. If I decide that this is a data set I want to do something special with or I am going to be using this data over and over in the future then I'll invest the time to write a script. UPDATE 9-29-214 nI started playing with an excellent new Python module. Its called can check it out at Page on . Painless to install. The module allows you to use Python and Excel and send data back and forth seamlessly. At least initially it looks as though it works really well but as with anything you'll probably eventually find bugs when you really start working with it. None the less at a superficial level it seems great. Why is this useful? Well like I said above cleaning data in Excel simply sucks and it's fraught with errors. To be fair using iPython notebooks have some downsides as well. For instance you can only scroll up and down. So if you get a large data set and you are cleaning it looking at columns inspecting values the notebook can be a bit slow to work in because of all the scrolling you need to do. This new module is nice because you can do your thing in Python and then send it over to Excel and look at it morefortably. Also often I explore data perhaps do a few groupbys and then get curious about something and want to do an ad hoc calc or two. Yes. You can do this easily enough in Python but the document tends to get cluttered with lots of cells quickly. Which means more scrolling and more time. Perhaps some of you are anal enough to keep everything neat and tidy I am not. When I am in the initial stages of doing an analysis things get messy (cells everywhere). Sending out a dataframe to Excel and playing around with it there is a nice option especially if you have long descriptive variable names as typing new lines of code can be a bit verbose. Anyway I thought this was an example of increasing theplementary nature of Excel and Pandas Notebook and wanted to share it.