Keep only certain rows #pandas

keep_only_certain_rows.py

# for scalar values
df.loc[df['column_name'] == some_value]

# for rows whose column value is in an iterable
df.loc[df['column_name'].isin(some_values)]

# Combine multiple conditions with &:
df.loc[(df['column_name'] == some_value) & df['other_column'].isin(some_values)]

# To select rows whose column value does not equal some_value, use !=:
df.loc[df['column_name'] != some_value]

# isin returns a boolean Series, so to select rows whose value is not in some_values, negate the boolean Series using ~:
df.loc[~df['column_name'].isin(some_values)]

# In this dataframe
#      A      B  C   D
# 0  foo    one  0   0
# 1  bar    one  1   2
# 2  foo    two  2   4
# 3  bar  three  3   6
# 4  foo    two  4   8
# 5  bar    two  5  10
# 6  foo    one  6  12
# 7  foo  three  7  14

# If you have multiple values you want to include, put them in a list (or more generally, any iterable) and use isin:
print(df.loc[df['B'].isin(['one','three'])])

# Note, however, that if you wish to do this many times, it is more efficient to make an index first, and then use df.loc:
df = df.set_index(['B'])
print(df.loc['one'])

# or, to include multiple values from the index use df.index.isin:
df.loc[df.index.isin(['one','two'])]