Read txt python dataframe

How to Read Data from Text File Into Pandas?

The following step-by-step example shows how to load data from a text file into Pandas. We can use:

• read_csv() function
  • it handles various delimiters, including commas, tabs, and spaces
  • read fixed-width formatted lines into DataFrame

  Let’s cover both cases into examples:

  read_csv — delimited file

  To read a text into Pandas DataFrame we can use method read_csv() and provide the separator:

  import pandas as pd df = pd.read_csv('data.txt', sep=',') 

  Where sep argument specifies the separator. Separator can be continuous — ‘\s+’ .

  Other useful parameters are:

  • header=None — does the file contain headers
  • names=[«a», «b», «c»] — the column names
  • skiprows=[0,1] — skip rows
  • index_col=True — use index from the file

  read_fwf — fixed-width file

  To read data from a fixed-width file in Pandas we can use read_fwf. Suppose we have a file ‘data.txt’ like:

  John 35 123 A Jane D 28 45 E Bob 42 678 D 

  We can see that columns are aligned by position rather than separated by delimiters. Since the columns are separated by fixed widths:

  we can’t use read_csv() with a separator. Instead we will:

  import pandas as pd colspecs = [(0, 7), (7, 9), (13, 17), (17, 18)] df = pd.read_fwf('data.txt', colspecs=colspecs, header=None, names=['name', 'age', 'score', 'class']) df 

  name	age	score	class
  0	John	35	123	A
  1	Jane D	28	45	E
  2	Bob	42	678	D

  

  Pandas read text file line by line

  To read a text file line by line into a pandas DataFrame we can:

  • create an empty DataFrame
  • create an iterator to read the file line by line
  • iterate over the iterator and append each line to the DataFrame
  • reset the index of the DataFrame

  import pandas as pd df = pd.DataFrame() iterator = pd.read_csv('data.txt', header=None, iterator=True, chunksize=1) for chunk in iterator: df = df.append(chunk) df = df.reset_index(drop=True) 

  Pandas read text file with pattern

  As an alternative we can use list comprehension to read files and filter it.

  Let’s work with the following file:

  John 35 123 A Pattern Jane D 28 45 E Bob 42 678 D End of pattern 

  We can find the numbers of the start and end lines by matching pattern:

  a=[] with open('data.txt',"r") as r: a=r.readlines() a=[x.replace("\n","") for x in a] start = a.index("Pattern") +1 end = a.index("End of pattern") start, end 

  After that we can read the file with read_fwf or read_csv and filter the lines:

  import pandas as pd df = pd.read_fwf('data.txt', colspecs=colspecs, header=None, names=['name', 'age', 'score', 'class']) df = df[start:end] 

  name	age	score	class
  2	Jane D	28	45	E
  3	Bob	42	678	D

  Summary

  We’ve seen three different ways of reading and loading text file into Pandas DataFrame. We covered how to read delimited or fixed-length files with Pandas.

  We also saw how to read text files line by line and how to filter csv or text file by pattern.

  By using DataScientYst — Data Science Simplified, you agree to our Cookie Policy.

  Источник

  How to Load data from txt to DataFrame with pandas?

  In this article, we will discuss multiple methods to load data from a text file to pandas DataFrame. We will try to cover multiple situations as well to take care while loading these text files into DataFrame.

  Table of Contents

  Introduction

  To quickly get started, let’s say we have a text file named “sample.txt”, the contents of the file look like the below.

  Name Designation Team Experience Shubham Data_Scientist Tech 5 Riti Data_Engineer Tech 7 Shanky Program_Manager Tech 2 Shreya Graphic_Designer Design 2 Aadi Backend_Developer Tech 11 Sim Data_Engineer Design 4

  Load txt file to DataFrame using pandas.read_csv() method

  The “read_csv” function from pandas is the most commonly used to read any CSV file. However, it can be used to read any text file as well. We just need to redefine a few attributes of the function to read the text files. Let’s quickly try below to read our “sample.txt” file.

  import pandas as pd # read txt file using read_csv function df = pd.read_csv("sample.txt", sep="\s+") print(df)

  Frequently Asked:

  Name Designation Team Experience 0 Shubham Data_Scientist Tech 5 1 Riti Data_Engineer Tech 7 2 Shanky Program_Manager Tech 2 3 Shreya Graphic_Designer Design 2 4 Aadi Backend_Developer Tech 11 5 Sim Data_Engineer Design 4

  As observed, we have used pandas.read_csv() function with an additional attribute called separator (“sep”). By default, it takes a comma as the separator to read CSV (comma separated) files, while we can change it to tab (“\t”) or spaces (“\s+”) to read other text files.

  In case, we don’t have a header defined in the text file, we can use the header attribute as “None”, otherwise it will by default use the first row as a header.

  import pandas as pd # read txt file using read_csv function # note that we have used skiprows to skip the header df = pd.read_csv("sample.txt", sep="\s+", header=None, skiprows=1) print(df)

  0 1 2 3 0 Shubham Data_Scientist Tech 5 1 Riti Data_Engineer Tech 7 2 Shanky Program_Manager Tech 2 3 Shreya Graphic_Designer Design 2 4 Aadi Backend_Developer Tech 11 5 Sim Data_Engineer Design 4

  Now, to define new column headers while reading the file, we can use the attribute “names” in the function below.

  import pandas as pd # read txt file using read_csv function # adding new column names while reading txt file df = pd.read_csv( "sample.txt", sep="\s+", header=None, skiprows=1, names = ["col1", "col2", "col3", "col4"]) print(df)

  col1 col2 col3 col4 0 Shubham Data_Scientist Tech 5 1 Riti Data_Engineer Tech 7 2 Shanky Program_Manager Tech 2 3 Shreya Graphic_Designer Design 2 4 Aadi Backend_Developer Tech 11 5 Sim Data_Engineer Design 4

  We have the new column names defined while reading the txt file.

  Load txt file to DataFrame using pandas.read_table() method

  The read_table() function from pandas is similar to the read_csv() function. Let’s quickly see a quick example to load the same txt file using the read_table function.

  import pandas as pd # read txt file using read_table function df = pd.read_table("sample.txt", sep="\s+") print(df)

  Name Designation Team Experience 0 Shubham Data_Scientist Tech 5 1 Riti Data_Engineer Tech 7 2 Shanky Program_Manager Tech 2 3 Shreya Graphic_Designer Design 2 4 Aadi Backend_Developer Tech 11 5 Sim Data_Engineer Design 4

  Load txt file to a DataFrame using pandas.read_fwf() method

  There can be multiple challenges while reading the text files. A few common includes cases such as either the column values also contain spaces or there is no common delimiter in the text file.

  Let’s look at the example below, where our sample.txt file where the column “Designation” contains values with spaces between them.

  Name Designation Team Experience Shubham Data Scientist Tech 5 Riti Data Engineer Tech 7 Shanky Program Manager Tech 2 Shreya Graphic Designer Design 2 Aadi Backend Developer Tech 11 Sim Data Engineer Design 4

  In such cases, if we use the read_csv function, it will result in separating that column into multiple columns as shown below.

  import pandas as pd # read txt file using read_csv function df = pd.read_csv("sample.txt", sep="\s+") print(df)

  Name Designation Team Experience Shubham Data Scientist Tech 5 Riti Data Engineer Tech 7 Shanky Program Manager Tech 2 Shreya Graphic Designer Design 2 Aadi Backend Developer Tech 11 Sim Data Engineer Design 4

  This is where, the read_fwf function from pandas comes in handy, which loads the width-formatted text files into pandas DataFrame easily. Let’s quickly try below.

  import pandas as pd # read txt file using read_table function df = pd.read_fwf("sample.txt") print(df)

  Name Designation Team Experience 0 Shubham Data Scientist Tech 5 1 Riti Data Engineer Tech 7 2 Shanky Program Manager Tech 2 3 Shreya Graphic Designer Design 2 4 Aadi Backend Developer Tech 11 5 Sim Data Engineer Design 4

  Here you go, we don’t even need to provide any separator in this case as it works on a fixed width-based separator.

  Summary

  In this article, we have discussed multiple ways to load a text file into DataFrame using pandas. Thanks.

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  Источник

  Как читать текстовый файл с помощью Pandas (включая примеры)

  

  Чтобы прочитать текстовый файл с пандами в Python, вы можете использовать следующий базовый синтаксис:

  df = pd.read_csv (" data.txt", sep=" ") 

  В этом руководстве представлено несколько примеров использования этой функции на практике.

  Чтение текстового файла с заголовком

  Предположим, у нас есть следующий текстовый файл с именем data.txt и заголовком:

  

  Чтобы прочитать этот файл в DataFrame pandas, мы можем использовать следующий синтаксис:

  import pandas as pd #read text file into pandas DataFrame df = pd.read_csv (" data.txt", sep=" ") #display DataFrame print(df) column1 column2 0 1 4 1 3 4 2 2 5 3 7 9 4 9 1 5 6 3 6 4 4 7 5 2 8 4 8 9 6 8 

  Мы можем распечатать класс DataFrame и найти количество строк и столбцов, используя следующий синтаксис:

  #display class of DataFrame print(type(df)) #display number of rows and columns in DataFrame df.shape (10, 2) 

  Мы видим, что df — это DataFrame pandas с 10 строками и 2 столбцами.

  Чтение текстового файла без заголовка

  Предположим, у нас есть следующий текстовый файл с именем data.txt без заголовков:

  

  Чтобы прочитать этот файл в DataFrame pandas, мы можем использовать следующий синтаксис:

  #read text file into pandas DataFrame df = pd.read_csv (" data.txt", sep="", header= None ) #display DataFrame print(df) 0 1 0 1 4 1 3 4 2 2 5 3 7 9 4 9 1 5 6 3 6 4 4 7 5 2 8 4 8 9 6 8 

  Поскольку в текстовом файле не было заголовков, Pandas просто назвали столбцы 0 и 1 .

  Прочитайте текстовый файл без заголовка и укажите имена столбцов

  При желании мы можем присвоить имена столбцам при импорте текстового файла с помощью аргумента имен :

  #read text file into pandas DataFrame and specify column names df = pd.read_csv (" data.txt", sep="", header= None, names=[" A", " B "] ) #display DataFrame print(df) A B 0 1 4 1 3 4 2 2 5 3 7 9 4 9 1 5 6 3 6 4 4 7 5 2 8 4 8 9 6 8 

  Источник

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