Iterate over pandas dataframe and apply condition

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Consider I have this dataframe, wherein I want to remove toy as a topic from the topics column and if there is row with a single topic as a toy , remove that row. How can we do that in pandas?

+---+-----------------------------------+-------------------------+
|   |           Comment                 |            Topic        |
+---+-----------------------------------+-------------------------+
| 1 |             -----                 | toy, bottle, vegetable  | 
| 2 |             -----                 | fruit, toy, electronics |  
| 3 |             -----                 | toy                     |  
| 4 |             -----                 | electronics, fruit      |  
| 5 |             -----                 | toy, electronic         |           
+---+-----------------------------------+-------------------------+

Try using str.replace with str.rstrip and ne inside [...]:

df['topic'] = df['topic'].str.replace('toy', ' ').str.replace(' , ', '').str.rstrip()
print(df[df['topic'].ne('')])

For loop in Pandas a.k.a. df.apply() | by Filip Geppert, If you use for loop in Pandas, something smells bad. Why However, what if we wanted to assign a particular value only for a given condition? Let's see how . iterrows() performs on the following DataFrame in terms of time� In Pandas Dataframe we can iterate an element in two ways: Iterating over rows; Iterating over columns; Iterating over rows : In order to iterate over rows, we can use three function iteritems(), iterrows(), itertuples() . These three function will help in iteration over rows.

lambda functions can come handy in such scenarios

df['topic'] = df['topic'].apply(lambda x: "" if len(x.split(','))==1 and x.split(',')[0]=='toy'))

Different ways to iterate over rows in Pandas Dataframe , Let's see the Different ways to iterate over rows in Pandas Dataframe : Method #1 : Using index attribute of the Dataframe . Method #6 : Using apply() method of the Dataframe. number of rows and columns of a Pandas dataframe � Count all rows or those that satisfy some condition in Pandas dataframe� I have a pandas dataframe composed of 8 columns (c1 to c7 and the last is called total). c1 to c7 are 0 and 1. The column total should be an assignment for the maximum number of 1 in a sequence within c1 to c7. c1 to c7 represent weekdays, hence 7 should then flip to 1. For example, if we would have and initial dataframe like df:

# create data dummy test data
data = {'comment':[1,2,3,4,5],
        'Topic':['toy, bottle, vegetable','fruit, toy, electronics','toy','electronics, fruit','toy, electronic']}

# create dataframe
df = pd.DataFrame(data)

# create function to tidy your data and remove toy
def remove_toy(row):
    row = [i.strip() for i in row.split(',')]
    row = [i for i in row if i != 'toy']
    return ', '.join(row)

# apply function to series
df['Topic'] = df['Topic'].apply(remove_toy)

#remove empy rows in the Topic series
df = df[df['Topic']!='']

Ways to apply an if condition in Pandas DataFrame, Different ways to iterate over rows in Pandas Dataframe � Python | Pandas DataFrame.fillna() to replace Null values in dataframe � Convert given� There are so many ways to iterate over the rows in Pandas dataframe. One very simple and intuitive way is: df = pd.DataFrame({'A':[1, 2, 3], 'B':[4, 5, 6], 'C':[7, 8, 9]}) print(df) for i in range(df.shape[0]): # For printing the second column print(df.iloc[i, 1]) # For printing more than one columns print(df.iloc[i, [0, 2]])

pandas.DataFrame.iterrows — pandas 1.1.0 documentation, Iterate over DataFrame rows as namedtuples of the values. To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns� np.select () method. np.where () takes condition-list and choice-list as an input and returns an array built from elements in choice-list, depending on conditions. We can use this method to create a DataFrame column based on given conditions in Pandas when we have two or more conditions. Python.

How to iterate through rows of a DataFrame in Pandas, We can iterate over rows in Pandas using index attribute, loc() , iloc() , iterrows(), itertuples() , iteritems() and apply() methods. Iterate Over columns in dataframe by index using iloc[] To iterate over the columns of a Dataframe by index we can iterate over a range i.e. 0 to Max number of columns then for each index we can select the columns contents using iloc[]. Let’s see how to iterate over all columns of dataframe from 0th index to last index i.e.

Pandas DataFrame – Iterate Rows – iterrows () To iterate through rows of a DataFrame, use DataFrame.iterrows () function which returns an iterator yielding index and row data for each row. In this tutorial, we shall go through examples demonstrating how to iterate over rows of a DataFrame. Example 1: Iterate through rows of Pandas DataFrame

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  • can you share the code that you have tried