How to filter specific set of rows values from csv using Pandas?

Question

I have following csv and i need to filter specific set of rows

table   entity_name node_name   src_name    table_col_name  look_up_indicator   type       keys
gw_policy   account ns0         fullname    insured_name       N                attribute   NA
gw_policy   polocy  ns1         agent_name  insured_id             N                attribute   NA
gw_policy   account ns2         phone_num   agent_phone        N                attribute   NA
gw_policy   account ns0         fullname    insured_name       N                attribute   NA
gw_policy   polocy  ns1         agent_name  agent              N                attribute   NA
gw_policy   account ns2         phone_num   a_phone            N                attribute   NA
gw_policy   account ns0         fullname    agen_name      N                attribute   NA
gw_policy   polocy  ns1         agent_name  agent              N                attribute   N

Now from above csv i need to get set of rows based on range of row name

ie, in this case i need to get the rows between two 'insured_name' and two 'agent', from the column name 'table_col_name'

so the result would look like

#Expected

# For the insured_name

insured_name
insured_id
agent_phone
insured_name

# For the agent
agent
a_phone
agen_name
agent

So how can achieve this using pandas?

Appreciated for the help?

Thanks

There are 3 rows with agent. How to decide which 2 agents to pick out of these two? — Mayank Porwal
– Mayank Porwal, Commented Dec 24, 2020 at 14:55

Mayank Porwal · Accepted Answer · 2020-12-24 16:38:50Z

Use df.index with df.loc:

In [2278]: insured_name_ix = df[df.table_col_name.eq('insured_name')].index

In [2283]: x = df.loc[insured_name_ix[0]: insured_name_ix[1]]

In [2284]: x
Out[2284]: 
       table entity_name node_name    src_name table_col_name look_up_indicator       type keys
0  gw_policy     account       ns0    fullname   insured_name                 N  attribute  NaN
1  gw_policy      polocy       ns1  agent_name          agent                 N  attribute  NaN
2  gw_policy     account       ns2   phone_num    agent_phone                 N  attribute  NaN
3  gw_policy     account       ns0    fullname   insured_name                 N  attribute  NaN

In [2317]: agent_ix = df[df.table_col_name.eq('agent')].index
In [2319]: y = df.loc[agent_ix[0]: agent_ix[1]]

In [2320]: y
Out[2320]: 
       table entity_name node_name    src_name table_col_name look_up_indicator       type keys
4  gw_policy      polocy       ns1  agent_name          agent                 N  attribute  NaN
5  gw_policy     account       ns2   phone_num        a_phone                 N  attribute  NaN
6  gw_policy     account       ns0    fullname      agen_name                 N  attribute  NaN
7  gw_policy      polocy       ns1  agent_name          agent                 N  attribute    N

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