I've been reading a tab-delimited data file in Windows with Pandas/Python without any problems. The data file contains notes in first three lines and then follows with a header. Show
I'm now trying to read this file with my Mac. (My first time using Python on Mac.) I get the following error.
If set the error_bad_lines argument for read_csv to False, I get the following information, which continues until the end of the last row.
Do I need to specify a value for the encoding argument? It seems as though I shouldn't have to because reading the file works fine on Windows. menu search toc more_vert Thanks for the thanks! close chevron_left Creating DataFrames Cookbook search keyboard_voice close Searching Tips Search for a recipe: Search for an API documentation: "@append" Search for code: "!dataframe" Apply a tag filter: "#python" Useful Shortcuts / to open search panel Esc to close search panel ↑↓ to navigate between search results ⌘d to clear all current filters ⌘Enter to expand content preview Doc Search Code Search Beta SORRY NOTHING FOUND! Voice search is only supported in Safari and Chrome. arrow_backShare Twitterchevron_left Creating DataFrames Cookbook Pandas chevron_right Cookbooks chevron_right DataFrame Cookbooks chevron_right Creating DataFrames Cookbook schedule Jul 1, 2022 Last updated local_offer Python●Pandas Tags tocTable of Contents expand_more Consider the following tab-delimited file called
A B 3 4 5 6
To read this file using
df = pd.read_csv("my_data.txt", sep="\t") df A B 0 3 4 1 5 6
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What is Pandas?pandas is a Python library containing a set of functions and specialised data structures that have been designed to help Python programmers to perform data analysis tasks in a structured way. Most of the things that pandas can do can be done with basic Python, but the collected set of pandas functions and data structure makes the data analysis tasks more consistent in terms of syntax and therefore aids readabilty. Particular features of pandas that we will be looking at over this and the next couple of episodes include:
If you are wondering why I write pandas with a lower case ‘p’ it is because it is the name of the package and Python is case sensitive. Importing the pandas libraryImporting the
pandas library is done in exactly the same way as for any other library. In almost all examples of Python code using the pandas library, it will have been imported and given an alias of Pandas data structuresThere are two main data structure used by pandas, they are the Series and the Dataframe. The Series equates in general to a vector or a list. The Dataframe is equivalent to a table. Each column in a pandas Dataframe is a pandas Series data structure. We will mainly be looking at the Dataframe. We can easily create a Pandas Dataframe by reading a .csv file Reading a csv fileWhen we read a csv dataset in base Python we did so by opening the dataset, reading and processing a record at a time and then closing the dataset after we had read the last record. Reading datasets in this way is slow and places all of the responsibility for extracting individual data items of information from the records on the programmer. The main advantage of this approach, however, is that you only have to store one dataset record in memory at a time. This means that if you have the time, you can process datasets of any size. In Pandas, csv files are read as complete datasets. You do not have to explicitly open and close the dataset. All of the dataset records are assembled into a Dataframe. If your dataset has column headers in the first record then these can be used as the Dataframe column names. You can explicitly state this in the parameters to the call, but pandas is usually able to infer that there ia a header row and use it automatically. For our examples in this episode we are going to use the SN7577.tab file. This is available for download here and the description of the file is available here We are going to read in our SN7577.tab file. Although this is a tab delimited file we will still use the pandas
Getting information about a DataframeYou can find out the type of the variable
You can see the contents by simply entering the variable name. You can see from the output that it is a tabular format. The column names have been taken from the first record of the file. On the left hand side is a column with no name. The entries here have been provided by pandas and act as an index to reference the individual rows of the Dataframe. The Another thing to notice about the display is that it is truncated. By default you will see the first and last 30 rows. For the columns you will always get the first few columns and typically the last few depending on display space. Similar information can be obtained with
You can obtain other basic information about your Dataframe of data with:
How do I read a tab delimited text file in Python?To read tab-separated values files with Python, we'll take advantage of the fact that they're similar to CSVs. We'll use Python's csv library and tell it to split things up with tabs instead of commas. Just set the delimiter argument to "\t" . That's it!
How do I read a delimited file in pandas?We can read data from a text file using read_table() in pandas. This function reads a general delimited file to a DataFrame object. This function is essentially the same as the read_csv() function but with the delimiter = '\t', instead of a comma by default.
How do I save pandas as tab delimited file?Approach :. Import the Pandas and Numpy modules.. Create a DataFrame using the DataFrame() method.. Save the DataFrame as a csv file using the to_csv() method with the parameter sep as “\t”.. Load the newly created CSV file using the read_csv() method as a DataFrame.. Display the new DataFrame.. Which of the following method of pandas is used to obtain data from a tab separated value file?Although this is a tab delimited file we will still use the pandas read_csv method, but we will explicitly tell the method that the separator is the tab character and not a comma which is the default.
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