Modify Pandas series through For loop

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Use:

item['Weight']=item.Weight.str.strip('kgs')

iterate over pandas dataframe and update the value, As iterrows() returns each row contents as series but it does not preserve dtypes of values in the rows. We can not modify something while� print (type (df ['age'])) # <class 'pandas.core.series.Series'>. When you apply a Series to a for loop, you can get its value in order. If you specify a column in the DataFrame and apply it to a for loop, you can get the value of that column in order. 1.

There is a built in method .str.strip() try:

item.str.rstrip('kgs')

Pandas : 6 Different ways to iterate over rows in a Dataframe , now I would like to iterate row by row and as I go through each row, the value of ifor in each row can change depending on some conditions� The pandas iterrows function returns a pandas Series for each row, with the down side of not preserving dtypes across rows. def loop_with_iterrows(df): temp = 0 for _, row in df.iterrows(): temp

Use Series.str.rstrip to remove kgs to the right of the values

item['Weight']=item.Weight.str.rstrip('kgs')

then whe can use Series.astype to convert to float or int:

item['Weight']=item.Weight.str.rstrip('kgs').astype(float)
#item['Weight']=item.Weight.str.rstrip('kgs').astype(int)

or pd.to_numeric with errors = 'coerce' and then check if there is any NaN value and what is its origin.

item['Weight']=pd.to_numeric(item.Weight.str.rstrip('kgs'),errors = 'coerce')

Update a dataframe in pandas while iterating row by row, Modify Series in place using values from passed Series. Uses non-NA values from passed Series to make updates. Aligns on index. Parameters. otherSeries, or� Create a function to assign letter grades. # Create a list to store the data grades = [] # For each row in the column, for row in df ['test_score']: # if more than a value, if row > 95: # Append a letter grade grades. append ('A') # else, if more than a value, elif row > 90: # Append a letter grade grades. append ('A-') # else, if more than a value, elif row > 85: # Append a letter grade

In the list comprehension method you are basically creating the complete list and assigning to weights columns so it works as expected.. although the method mentioned in other answers is more efficient.

This first method does not work because you are assigning one weight at a time to whole column not a list.

pandas.Series.update — pandas 1.1.1 documentation, inplacebool, default False. If True, in place. Note: this will modify any other views on this object (e.g. a column from a DataFrame). Returns the caller if this is True� Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

pandas.Series.replace — pandas 1.1.1 documentation, pandas.Series.iteritems¶ Series.iteritems [source] ¶ Lazily iterate over (index, value) tuples. This method returns an iterable tuple (index, value). This is convenient if you want to create a lazy iterator.

Varun March 10, 2019 Pandas : Loop or Iterate over all or certain columns of a dataframe 2019-03-10T19:11:21+05:30 Pandas, Python No Comment In this article we will different ways to iterate over all or certain columns of a Dataframe.

Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index. pandas.Series. A pandas Series can be created using the following constructor − pandas.Series( data, index, dtype, copy) The parameters of the constructor are as follows −

Comments

Tried with Series.str.strip() without For loop and it worked, thanks for the suggestion. However, i wanted to know why for For loop did not work, and thanks SubhashR for explaining me that.
If something is trivial for you it not means it is trivial for others ;)