Unnamed column and Nan in Pandas

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i'm getting Unnamed and Nan in output when i try to print the headers of .csv file.

import pandas as pd

df = pd.read_csv('testextract.csv', error_bad_lines=False,sep=' ',dtype=unicode,index_col=0,low_memory=False)
print(df.head())

Output :

 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6  \
��T                                                                     
NaN        NaN        NaN        NaN        NaN        NaN        NaN   
NaN        NaN        NaN        NaN        NaN        NaN        NaN

data = df.loc[:, ~df.columns.str.contains('^Unnamed')]
print(data)

10. Working with Data I: Data Cleaning, Pandas allows easy organization of data in the spirit of DataFrame concept in R. You can Area Frequency Unnamed: 2 Unnamed: 3 0 Accounting 73.0 NaN NaN 1 Finance The DataFrame has an index for each row and a column header. if the first column in the CSV file has index values, then you can do this instead: First, find the columns that have 'unnamed', then drop those columns. Note: You should Add inplace = True to the .drop parameters as well. The pandas.DataFrame.dropna function removes missing values (e.g. NaN, NaT).


You are reading a csv file and using the separater as ' '. Use the below code pd.read_csv(file_name,encoding = 'UTF-8')

Remove Unnamed columns in pandas dataframe [duplicate], df = df.loc[:, ~df.columns.str.contains('^Unnamed')] In [162]: df Out[162]: colA ColB colC colD colE colF colG 0 44 45 26 26 40 26 46 1 47 16 38  Because NaN is a float, a column of integers with even one missing values is cast to floating-point dtype (see Support for integer NA for more). Pandas provides a nullable integer array, which can be used by explicitly requesting the dtype:


I've come across the same error. You have to change your encoding to ensure it's UTF-8. You can do this two ways:

  1. Use the encoding method from Pandas i.e. for your example:
df = pd.read_csv('testextract.csv', encoding, errors='strict')
  1. Open the CSV file in a spreadsheet application and save as in UTF-8. Then, run your code again.

Hope this helps.

Three or more unnamed fields block loc assignment · Issue #13017 , B > 500] = None In [6]: df Out[6]: A B 0 1 4.0 1 2 5.0 2 3 NaN TST: Add test for mangling of unnamed columns (pandas-dev#23485) … How to get rid of Unnamed: column in a pandas dataframe. I have a situation wherein sometimes when I read a csv from df I get an unwanted index-like column named unnamed:0. This is very annoying! I have tried


Pandas cheat sheet, Here is a pandas cheat sheet of the most common data operations in pandas. Convert column data to integer (nan values are set to -1):. df['col']  pandas returning the unnamed columns. The following is example of data I have in excel sheet. I am trying to get the columns name using the following code: [A, B, C, 'Unnamed: 3', 'unnamed 4', 'unnamed 5', ..] I checked the excel sheet, there is only three columns named A, B, and C. Other columns are blank.


Pandas Read CSV Tutorial: How to Read and Write, In the image above, we can see that we get a column named 'Unnamed: 0'. Furthermore,  In this article we will discuss how to remove rows from a dataframe with missing value or NaN in any, all or few selected columns. Python’s pandas library provides a function to remove rows or columns from a dataframe which contain missing values or NaN i.e. It removes rows or columns (based on arguments) with missing values / NaN.


pandas.read_excel, Row (0-indexed) to use for the column labels of the parsed DataFrame. If a list of Whether or not to include the default NaN values when parsing the data. Depending on the scenario, you may use either of the 4 methods below in order to replace NaN values with zeros in pandas DataFrame: (1) For a single column using pandas: df['DataFrame Column'] = df['DataFrame Column'].fillna(0) (2) For a single column using numpy: df['DataFrame Column'] = df['DataFrame Column'].replace(np.nan, 0) (3) For an entire DataFrame using pandas: df.fillna(0) (4) For an entire DataFrame using numpy: df.replace(np.nan,0)