Select rows from a DataFrame based on column values?
#To select rows whose column value equals a scalar, some_value, use ==:
df.loc[df['A'] == 'foo']
#To select rows whose column value is in an iterable, some_values, use isin:
df.loc[df['B'].isin(['one','three'])]
#Combine multiple conditions with &:
df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)]pandas return row
1. Selecting data by row numbers (.iloc)
# myrow = data.iloc[<row selection>]
myrow = data.iloc[7]
myrow = data.iloc[0:9]
2. Selecting data by label or by a conditional statement (.loc)
# myrow = data.loc[<row selection.]
myrow = data.loc['University ABC']
3. Selecting in a hybrid approach (.ix) (now Deprecated in Pandas 0.20.1)
# Works like a .loc but also accepts integers - may lead to unexpected resultsCopyright © 2021 Codeinu
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
