Looping over dataframe and selecting rows based on substring in Pandas

Question

I have a dataframe with 5 columns, one of which is 'TABLE_NAME'. That column has values such as:

A_value1
B_value1
B_value2
A_value150

I want to print those that start with 'A_' only.

I tried this but its returning the following:

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Here is the code:

value = 'A_'
for index, row in df.iterrows():
    if df.loc[df['TABLE_NAME'].str.contains(value)]:
        print('y')
    else:
        print('n')

[Series Accessors](https://pandas.pydata.org/pandas-docs/stable/reference/series.html#accessors) — wwii, Jan 25 '20 at 02:08
Explicit loops should be a last resort, read the Pandas docs. — AMC, Jan 25 '20 at 03:40
Does this answer your question? [Truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()](https://stackoverflow.com/questions/36921951/truth-value-of-a-series-is-ambiguous-use-a-empty-a-bool-a-item-a-any-o) — AMC, Jan 25 '20 at 03:40

score 1 · Accepted Answer · answered Jan 25 '20 at 02:00

You can use str.startswith:

df.loc[df.TABLE_NAME.str.startswith('A_')]

If you want to use your for loop, you can do:

value = 'A_'
for index, row in df.iterrows():
    if row['TABLE_NAME'].startswith(value):
        print('y')
    else:
        print('n')

1 Answers1