Python Pandas, write DataFrame to fixed-width file (to_fwf?)

how to write dataframe to a text file in python
pandas export dataframe to text file
r write fixed width file
how to convert dataframe to txt file in python
pandas write series to text file
fixed width format pandas
read fixed-width file pandas
pandas split column fixed width

I see that Pandas has read_fwf, but does it have something like DataFrame.to_fwf? I'm looking for support for field width, numerical precision, and string justification. It seems that DataFrame.to_csv doesn't do this. numpy.savetxt does, but I wouldn't want to do:

numpy.savetxt('myfile.txt', mydataframe.to_records(), fmt='some format')

That just seems wrong. Your ideas are much appreciated.

Until someone implements this in pandas, you can use the tabulate package:

import pandas as pd
from tabulate import tabulate

def to_fwf(df, fname):
    content = tabulate(df.values.tolist(), list(df.columns), tablefmt="plain")
    open(fname, "w").write(content)

pd.DataFrame.to_fwf = to_fwf

Python Pandas, write DataFrame to fixed-width file (to_fwf?), Python Pandas, write DataFrame to fixed-width file (to_fwf?) 由流过昼夜 提交 import pandas as pd from tabulate import tabulate def to_fwf(df, fname): content� I need this data in a flat file in below format. Date Temperature_city_1 Temperature_city_2 Temperature_city_3 Which_destination 20140910 80 32 40 1 20140911 100 50 36 2 I am trying to use pandas and write this data to a flat file but no luck. Tried sample code but no luck

For custom format for each column you can set format for whole line. fmt param provides formatting for each line

with open('output.dat') as ofile:
     fmt = '%.0f %02.0f %4.1f %3.0f %4.0f %4.1f %4.0f %4.1f %4.0f'
     np.savetxt(ofile, df.values, fmt=fmt)

Add to_fwf support � Issue #10415 � pandas-dev/pandas � GitHub, Right now pandas can read fwf but there is no to_fwf. to_string() not easily reversible for multi-index DataFrames #25570 jbrockmendel added IO Fixed Width and removed IO CSV labels on Dec 11, Sign in to comment. Python Pandas, write DataFrame to fixed-width file (to_fwf?) (5 answers) Closed 3 years ago . I am trying to find the best way to write formatted data to a .csv or even a .txt file.

Python, Pandas : write content of DataFrame into text File

The question aboves answer helped me. It is not the best, but until to_fwf exists this will do the trick for me...

np.savetxt(r'c:\data\np.txt', df.values, fmt='%d')

or

np.savetxt(r'c:\data\np.txt', df.values, fmt='%10.5f')

IO Tools (Text, CSV, HDF5, ), colspecs: A list of pairs (tuples) giving the extents of the fixed-width fields of each line as In order to parse this file into a DataFrame, we simply need to supply the column which columns to write; col_space default None, minimum width of each column. engine) sql.execute('INSERT INTO table_name VALUES(?, ?, ?) Python read_fwf - 30 examples found. These are the top rated real world Python examples of pandas.read_fwf extracted from open source projects. You can rate examples to help us improve the quality of examples.

I'm sure you found a workaround for this issue but for anyone else who is curious... If you write the DF into a list, you can write it out to a file by giving the 'format as a string'.format(list indices) eg:

df=df.fillna('')
outF = 'output.txt'      
dbOut = open(temp, 'w')
v = df.values.T.tolist()        
for i in range(0,dfRows):       
    dbOut.write(( \
    '{:7.2f}{:>6.2f}{:>2.0f}{:>4.0f}{:>5.0f}{:6.2f}{:6.2f}{:6.2f}{:6.1f {:>15}{:>60}'\
    .format(v[0][i],v[1][i],v[2][i],v[3][i],v[4][i],v[5][i],v[6][i],v[7][i],v[8][i],\
    v[9][i],v[10][i]) ))
    dbOut.write("\n")
dbOut.close

Just make sure to match up each index with the correct format :)

Hope that helps!

pandas.read_fwf — pandas 1.1.0 documentation, colspecs: a list of pairs (tuples), giving the extents of the fixed-width fields of each In order to parse this file into a DataFrame, we simply need to supply the column path: A string path to the file to write nanRep: A string representation of a� The following are 40 code examples for showing how to use pandas.read_fwf().They are from open source Python projects. You can vote up the examples you like or vote down the ones you don't like.

found a very simple solution! (Python). In the code snapped I am trying to write a DataFrame to a positional File. "finalDataFrame.values.tolist()" will return u a list in which each row of the DataFrame is turn into an another list just a [['Camry',2019,'Toyota'],['Mustang','2016','Ford']]. after that with the help of for loop and if statement i am trying to set its fix length. rest is obvious!

 with open (FilePath,'w') as f:
    for i in finalDataFrame.values.tolist():
        widths=(0,0,0,0,0,0,0)
        if i[2] == 'nan':
            i[2]=''
            for h in range(7):
                i[2]= i[2] + ' '
        else:
            x=7-len(str(i[2]))
            a=''
            for k in range(x):
               a=a+' '
            i[2]=str(i[2])+a

        if i[3] == '':
            i[3]=''
            for h in range(25):
                i[3]=i[3]+' '
        else:
            x = 25 - len(i[3])
            print(x)
            a = ''
            for k in range(x):
                a = a + ' '
            print(a)
            i[3] = i[3] + a


        i[4] = str(i[4])[:10]

        q="".join("%*s" % i for i in zip(widths, i))
        f.write(q+'\n')

Python Pandas、fixed-widthファイルにDataFrameを書き込む(to_fwf?), Read a table of fixed-width formatted lines into DataFrame. New in version 0.24.0. **kwdsoptional Write DataFrame to a comma-separated values (csv) file. pandas.read_fwf¶ pandas.read_fwf (filepath_or_buffer: Union[str, pathlib.Path, IO[~ AnyStr]], colspecs = 'infer', widths = None, infer_nrows = 100, ** kwds) [source] ¶ Read a table of fixed-width formatted lines into DataFrame. Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the online

Python Pandas, escriba DataFrame en el archivo de ancho fijo , Pandasにはread_fwfがありますが、DataFrame.to_fwfのようなものがありますか? Python Pandas, write DataFrame to fixed-width file (to_fwf?) You can use pandas.DataFrame.to_csv(), and setting both index and header to False: In [97]: print df.to_csv(sep=' ', index=False, header=False) 18 55 1 70 18 55 2 67 18 57 2 75 18 58 1 35 19 54 2 70 pandas.DataFrame.to_csv can write to a file directly, for more info you can refer to the docs linked above.

python, Python, Pandas : write content of DataFrame into text File. La pregunta aboves respuesta me ayud�. No es el mejor, pero hasta to_fwf existe esto va a hacer el� The simple task of adding a row to a pandas.DataFrame object seems to be hard to accomplish. There are 3 stackoverflow questions relating to this, none of which give a working answer.

Fixed width file python, 我看到Pandas有read fwf ,但它有像DataFrame.to fwf这样的东西吗我正在寻找对 Python Pandas, write DataFrame to fixed-width file (to_fwf?) I've currently written lots to the existing txt file by using .write() and would like to print the dataframe to the file and then continue adding more afterwards. Other methods write a separate txt file solely for the dataframe – Alejandro Rodriguez Aug 13 '18 at 20:20

Comments
  • take a look at the to_string method so see if you can do what you want.
  • This looks close. It seems that I'd have to give a formatter function for each column if any two float or string columns had different formats. It would do the trick, it just looks a little unwieldy. I'd hoped I was missing something. Thanks!
  • pandas df.to_csv has a sep =" " parameter that changes the comma to anything else in this case a space or empty string. That in conjunction with the formatter of the method should do it.
  • @Joop Actually with the df.to_csv() method using empty string as delimiter gives the error TypeError: delimiter must be set.
  • true.. passing empty string to method would create a mess do ignore my reference to empty string. maybe trying pandas "to_string" method would help. if has formatters parameter that is pretty good
  • This worked elegantly for me. So much saner that messing around with string-space-formatting manually.
  • IMO this is better than tabulate since numpy is included with pandas so doesn't require an additional library