Python Pandas, Series and DataFrame Question (NO Loops, No If Statements, No List Comprehensions)

The file bank.csv contains data about bank customers. The last column ('Personal Loan') indicates whether or not the customer was approved for a personal loan or not. Write a function named loan_by_zip that accepts 3 parameters: a file name, a minimum number of records, and a percentage approval rate. The function should return a DataFrame of those zip codes for which we meet the minimum number of records and the loan approval rate for that zip code (1 = approved, 0 = not approved).

Find below the file, bank.csv. Please note this is only a fraction of the file as it is too large to paste here.

ID	Age	Experience	Income	ZIP Code	Family	CCAvg	Education	Mortgage	Securities Account	CD Account	Online	CreditCard	Personal Loan
2001	28	2	22	95670	1	0.1	2	0	0	0	1	0	0
2002	44	17	128	94928	2	3.25	2	0	0	0	0	1	1
2003	30	4	142	92126	3	4.2	1	359	0	0	0	0	1
2004	44	20	124	90277	1	4.7	1	0	0	0	1	1	0
2005	30	4	44	92704	1	1.9	3	143	0	0	0	0	0
2006	47	23	170	90254	2	6.5	2	0	0	1	1	1	1
2007	64	39	75	94720	4	0.1	2	0	0	1	1	1	0
2008	48	21	78	94010	3	2	2	0	0	0	1	1	0
2009	63	38	31	92037	1	1.1	3	125	0	0	1	0	0
2010	25	0	99	92735	1	1.9	1	323	0	0	0	0	0
2011	61	36	41	96001	2	1.5	1	0	0	0	0	0	0
2012	46	21	39	92507	4	0	2	0	0	0	1	0	0
2013	57	31	51	93943	1	1.4	1	0	0	0	1	1	0
2014	40	15	52	92691	3	0.8	3	113	0	0	1	0	0
2015	49	19	169	95054	3	5.67	3	167	0	1	0	1	1
2016	30	5	141	95747	1	0.8	1	0	0	0	1	0	0
2017	41	17	93	92835	4	0.8	1	218	0	0	0	0	0
2018	42	15	14	92064	3	1	2	0	1	0	0	0	0
2019	63	39	160	90089	2	2.1	1	0	0	0	1	1	0
2020	43	17	44	94611	1	0.2	1	0	1	1	1	0	0
2021	59	34	33	94303	3	0.2	1	0	1	0	0	0	0
2022	46	20	103	91380	4	4.8	3	0	0	0	1	0	1
2023	33	3	71	93561	4	1.8	3	236	0	0	0	0	0
2024	55	29	55	94720	1	0.2	1	151	0	0	1	0	0
2025	36	12	113	94305	4	0.2	1	0	0	0	0	0	1
2026	47	20	79	94720	3	2	2	185	1	0	0	0	0
2027	59	33	80	93907	2	0.7	2	0	0	0	1	0	0
2028	38	12	179	94596	2	0	1	380	0	0	0	0	0
2029	42	17	9	91710	2	0	3	0	0	0	0	0	0
2030	30	3	61	92152	4	2	2	0	0	0	1	0	0
2031	63	38	111	95814	2	3.9	1	207	1	1	1	1	0
2032	60	35	80	94608	3	0.5	1	0	0	0	1	0	0
2033	62	37	32	90266	3	0.2	1	0	1	0	0	1	0
2034	49	23	83	92126	1	0.3	1	0	0	0	1	1	0
2035	59	33	91	92821	4	1.9	2	329	0	0	0	0	0
2036	36	10	29	93065	4	1	1	0	0	0	1	1	0
2037	46	19	19	94305	3	0.67	2	0	0	0	1	0	0
2038	35	8	52	95616	2	1	2	0	0	0	0	1	0
2039	50	24	150	94551	1	7.3	1	0	0	0	1	1	0
2040	51	25	32	91605	2	0.4	3	0	0	0	1	0	0
2041	41	16	91	94720	3	0.5	3	0	0	0	0	0	0
2042	45	20	180	95403	3	8.5	2	535	0	0	0	0	1
2043	41	17	121	94102	1	0.3	1	0	0	0	1	0	0
2044	57	32	25	90049	2	0.2	3	0	0	0	1	1	0
2045	51	25	102	92677	1	0.3	1	0	0	0	1	0	0
2046	52	28	44	95051	4	0.9	2	107	0	0	1	0	0
2047	43	16	161	95134	3	8	2	0	0	1	1	1	1
2048	63	38	134	90640	3	4	2	0	0	0	0	1	1
2049	28	4	43	94803	1	1.8	2	0	0	0	1	1	0
2050	43	18	94	92717	4	1.1	2	0	0	0	1	0	0
2051	41	15	29	94024	2	0.8	3	98	0	0	0	0	0
2052	34	8	38	90018	4	0.2	1	0	0	0	0	0	0
2053	28	3	120	94080	1	0.8	1	170	0	0	0	0	0
2054	58	32	85	92110	2	2	1	161	1	1	1	1	0
2055	39	15	89	92126	2	1.9	1	0	0	0	0	0	0
2056	49	23	25	90274	1	1.4	3	0	0	0	1	0	0
2057	33	8	20	92691	3	1.3	1	83	0	0	0	1	0
2058	37	12	125	91754	2	3.9	1	0	0	0	1	1	0
2059	33	7	18	92093	1	0.6	3	0	0	0	0	0	0
2060	28	3	173	92121	2	6.7	1	222	0	0	1	0	0
2061	54	29	34	92093	4	0.1	3	0	0	0	1	1	0
2062	63	38	159	93950	4	4.9	2	111	0	0	0	0	1
2063	57	31	55	92521	3	2.5	1	219	0	1	1	1	0
2064	56	30	32	94080	2	0.4	3	0	0	0	1	0	0
2065	54	29	65	94545	4	1.8	3	0	0	0	0	1	0
2066	29	5	83	92354	3	1.5	1	0	0	0	1	1	0
2067	41	16	30	95814	2	1.4	2	0	0	0	0	1	0
2068	58	32	180	91770	1	2.9	1	0	0	0	0	1	0
2069	61	37	13	90024	2	0.3	3	0	0	0	1	0	0
2070	30	4	35	90059	4	0.8	1	0	0	0	1	1	0
2071	62	37	95	91107	3	0.5	1	0	0	0	0	0	0
2072	52	28	83	94705	1	0	1	0	0	0	1	0	0
2073	29	3	39	95831	4	0.2	1	137	0	0	1	1	0
2074	46	20	54	91107	1	0.7	3	154	0	0	1	1	0
2075	52	27	81	91942	1	1.3	3	293	0	0	0	1	0
2076	40	16	53	94123	4	2	3	0	0	0	0	0	0
2077	49	23	119	91030	1	7.3	1	398	0	0	0	0	0
2078	34	9	160	94108	4	8	3	0	0	0	0	1	1
2079	35	11	21	95814	2	1	2	0	0	0	0	0	0
2080	26	2	40	94132	1	1	3	0	0	0	1	0	0
2081	65	40	69	91706	4	0.1	2	0	0	0	1	0	0
2082	52	27	45	95006	1	1.3	2	0	0	0	0	0	0
2083	32	7	55	91301	4	2	2	0	0	0	1	0	0
2084	31	7	38	94025	1	0.2	3	0	0	0	1	0	0
2085	36	9	44	93907	4	1	2	101	1	0	1	0	0
2086	50	24	45	94105	3	0.6	2	117	0	0	1	0	0
2087	36	12	84	90291	1	0.8	2	0	0	0	1	1	0
2088	51	27	188	94305	2	6.9	2	343	0	0	1	0	1
2089	39	9	29	94701	3	2	3	151	1	0	0	0	0
2090	53	29	95	94304	1	2.7	2	0	0	0	1	0	0
2091	50	25	79	95023	1	2.9	1	307	0	0	0	1	0
2092	31	4	41	91360	1	2	2	0	1	0	1	0	0
2093	53	23	19	92673	4	0.4	3	84	0	0	1	0	0
2094	48	23	75	94111	4	3.6	3	0	0	0	1	1	0
2095	57	31	64	90024	3	2.5	1	208	0	0	0	1	0
2096	47	21	174	94025	4	3.2	3	0	0	0	0	0	1
2097	55	29	54	95051	2	2.3	3	93	0	0	0	0	0
2098	37	11	14	90740	3	0.1	2	113	0	0	1	0	0
2099	59	35	94	90089	1	3.8	1	272	0	0	0	0	0
2100	53	29	10	90095	2	0.4	1	0	0	0	0	0	0
2101	31	6	145	93940	1	0.8	1	84	0	0	1	0	0
2102	35	5	203	95032	1	10	3	0	0	0	0	0	1
2103	25	-1	81	92647	2	1.6	3	0	0	0	1	1	0
2104	37	13	153	90630	2	6.5	1	0	0	0	1	1	0
2105	40	14	58	90245	4	0.2	3	0	0	0	1	0	0
2106	31	5	49	94114	4	1.8	3	0	0	0	1	1	0
2107	62	38	132	90210	1	2.9	1	0	0	0	0	0	0
2108	41	17	85	90291	4	0.2	3	229	0	0	0	0	0
2109	56	32	85	94132	3	2.67	1	0	1	0	1	0	0
2110	47	23	178	93014	1	6.5	3	0	0	0	0	0	1
2111	28	4	104	94301	3	2.5	1	0	0	0	0	0	0
2112	60	34	40	94105	1	1.6	1	0	0	0	1	0	0
2113	27	2	103	93117	1	1.9	1	120	0	0	1	0	0
2114	57	33	25	92064	2	1	1	0	0	0	1	0	0
2115	62	36	69	95039	2	1.7	3	0	0	0	1	0	0
2116	57	31	30	95070	3	1.4	1	0	0	0	0	0	0
2117	44	17	70	94920	3	2.67	2	0	0	0	0	0	0
2118	31	7	15	91380	3	0.9	3	0	0	0	1	0	0
2119	31	5	125	91320	2	1.3	1	0	0	0	1	1	0
2120	39	13	50	94923	3	0.5	3	0	0	0	0	0	0
2121	41	17	44	93106	1	0.3	3	0	0	0	1	0	0
2122	41	17	38	92182	4	2.2	2	180	0	0	0	1	0
2123	55	29	64	93437	3	0.8	1	119	0	0	0	1	0
2124	28	2	9	95014	1	0.1	2	0	0	0	1	0	0
2125	35	9	44	92054	3	0.9	1	89	0	0	0	0	0
2126	44	20	93	91910	4	0.8	1	101	0	0	1	0	0
2127	44	19	83	92121	4	0.4	1	141	0	0	0	0	0
2128	40	14	179	94720	2	0	1	0	0	0	0	0	0
2129	65	40	40	94104	1	1.1	3	0	0	0	0	0	0
2130	35	10	58	91754	4	0.7	3	232	0	0	0	0	0
2131	55	31	74	94607	3	2.67	1	0	0	0	0	1	0
2132	55	31	15	95747	1	0.2	1	0	0	0	0	0	0
2133	59	35	11	94949	2	1	1	0	0	0	0	1	0
2134	39	15	41	95035	1	2	2	176	0	0	1	0	0
2135	50	24	68	95821	1	1.5	2	120	0	0	1	1	0
2136	45	15	28	95039	1	0.75	3	0	1	0	0	0	0
2137	50	26	115	95008	1	1.2	3	0	0	0	0	1	1
2138	65	40	83	92354	4	0.1	2	247	0	0	1	0	0
2139	36	11	40	93611	2	1.1	2	166	1	0	0	0	0
2140	57	32	113	91768	1	0.1	3	0	0	0	1	0	0
2141	53	27	89	92130	1	0.8	3	0	1	0	1	0	0
2142	28	4	38	92109	4	1.6	1	0	0	0	0	0	0
2143	55	31	62	93943	4	1.5	1	0	0	0	1	0	0
2144	56	31	65	92093	3	1.7	1	109	0	0	0	0	0
2145	33	6	168	94720	3	5.67	2	0	1	1	1	0	1
2146	57	32	40	94720	3	1.7	1	0	0	0	1	1	0
2147	27	3	30	93108	1	1	3	80	0	0	0	0	0
2148	27	3	20	92007	4	1	1	0	0	0	0	0	0
2149	54	30	58	92007	2	3.2	3	0	0	0	0	0	0
2150	48	22	150	95039	1	7.3	1	193	0	0	0	0	0
2151	62	38	54	91320	1	0.8	1	0	1	0	0	1	0
2152	41	16	19	91730	2	0.3	2	105	0	0	0	0	0
2153	62	38	30	94304	3	0.1	3	128	0	0	1	0	0
2154	40	14	123	90041	1	5.2	1	0	0	0	1	0	0
2155	32	8	45	94558	1	2.4	2	0	0	0	0	0	0
2156	62	38	154	94305	1	2.9	1	0	0	0	1	0	0
2157	35	11	93	90747	2	2.7	1	0	0	0	1	1	0
2158	25	0	71	93727	4	0.2	1	78	1	0	0	0	0
2159	50	25	83	94720	4	3.1	1	0	0	0	0	1	1
2160	61	35	99	94085	1	4.8	3	255	0	0	0	1	1
2161	43	17	55	93933	3	2.2	2	0	0	0	0	0	0
2162	52	28	38	94131	4	0.9	2	95	0	0	1	1	0
2163	39	13	74	95008	3	0.9	2	155	0	0	1	0	0
2164	33	3	69	92161	4	1.8	3	0	0	0	1	0	0
2165	27	3	104	92007	2	2.5	1	184	1	0	1	0	0
2166	27	0	38	95929	4	1	3	154	0	0	1	0	0
2167	32	8	25	93524	3	0.9	3	0	0	0	1	1	0
2168	65	40	162	94596	1	1.3	1	0	0	0	1	0	0
2169	55	29	64	93063	4	2.6	3	0	0	0	1	0	0
2170	52	27	30	94305	2	0.7	2	0	0	0	1	1	0
2171	39	13	52	95039	3	0.5	3	0	1	0	0	1	0
2172	35	11	42	93108	1	1.5	3	0	0	0	1	0	0
2173	39	15	79	92028	2	1.8	2	219	0	0	0	0	0
2174	34	10	34	93407	1	1.7	1	164	0	0	0	0	0
2175	30	5	123	95605	2	3.1	1	0	0	0	1	0	0
2176	37	12	160	94305	2	3.3	1	0	0	0	0	0	0
2177	41	14	51	91320	3	2.33	2	0	0	0	1	0	0
2178	31	7	108	94507	1	4	1	0	0	0	1	0	0
2179	37	13	158	93943	2	2.3	2	0	0	1	1	1	1
2180	49	23	68	90024	1	1.5	2	0	0	0	0	0	0
2181	58	33	42	91380	2	1.6	3	0	0	0	1	0	0
2182	45	15	32	94143	1	0.75	3	105	0	0	1	0	0
2183	40	14	22	94566	2	1.4	3	0	0	0	0	0	0
2184	34	8	29	90025	2	2	3	0	0	0	1	1	0
2185	62	36	183	90095	2	3.4	3	0	0	0	0	0	1
2186	54	30	69	92009	1	1.6	3	0	0	0	1	1	0
2187	26	2	92	96001	2	0.2	1	0	0	0	1	0	0
2188	54	30	40	90024	2	1	3	0	0	0	0	0	0
2189	29	4	9	92037	4	0.5	3	86	0	0	1	1	0
2190	48	23	128	94309	1	0.6	1	0	0	0	1	1	0
2191	27	3	110	96150	2	0.2	1	294	1	0	0	1	0
2192	42	18	171	90027	2	8	1	0	0	0	1	0	0
2193	25	1	13	95814	4	1	1	95	0	0	0	1	0
2194	45	19	25	94609	2	0.1	3	102	0	0	1	0	0
2195	34	9	123	94553	1	1.6	2	0	0	0	1	0	1
2196	51	27	33	92037	4	0.2	1	83	0	0	1	0	0
2197	51	24	189	95211	4	4.75	2	0	0	0	1	0	1
2198	60	35	34	94102	1	0.3	3	0	0	0	1	0	0
2199	59	35	58	91355	1	0	2	0	0	0	1	0	0
2200	49	24	51	91016	1	1.3	2	98	0	0	0	1	0
2201	50	25	29	90095	2	1.3	1	0	0	0	0	0	0
2202	41	16	111	92009	2	0.4	1	0	0	0	0	0	0
2203	49	24	43	94709	4	1.9	3	0	0	0	1	0	0
2204	50	25	130	91320	1	0.6	1	311	0	0	0	0	0
2205	63	37	20	94704	2	0.4	1	76	0	0	0	0	0
2206	63	37	101	95819	2	2.8	1	0	0	0	0	0	0
2207	33	7	48	92831	4	2.2	2	207	0	0	0	0	0
2208	38	12	180	90245	1	2.8	3	158	0	0	1	0	1
2209	64	40	92	91109	2	0	3	185	1	0	1	0	0
2210	36	10	33	94080	3	0.9	1	0	0	0	0	0	0
2211	58	33	51	95006	2	1.9	2	0	0	0	0	0	0
2212	39	14	31	92717	2	1.4	2	94	0	0	1	1	0
2213	46	22	83	95060	1	2.7	1	0	0	0	1	0	0
2214	61	37	45	94610	1	0.8	1	0	0	0	0	0	0
2215	53	27	89	92735	1	0.8	3	146	0	0	1	1	0
2216	28	3	193	94501	3	4	2	0	0	0	1	0	1
2217	64	40	89	94707	1	3.8	1	0	0	0	0	0	0
2218	48	24	162	91355	4	3.3	2	446	0	1	1	0	1
2219	38	13	9	92634	2	0.3	2	0	0	0	0	0	0
2220	52	22	58	93101	4	2	3	223	0	0	1	0	0
2221	65	40	80	94105	1	0.8	3	0	0	0	1	0	0
2222	59	33	73	92056	2	1.7	3	0	0	0	1	1	0
2223	45	20	41	95008	1	0.3	1	0	0	0	1	0	0
2224	53	28	74	91711	3	2	2	0	1	0	0	0	0
2225	38	12	29	92084	2	1.4	3	0	0	0	1	0	0
2226	54	24	25	90505	4	0.4	3	115	0	0	0	0	0
2227	25	1	98	90717	1	5.4	1	0	0	0	1	0	0
2228	61	35	59	90840	4	1.7	2	0	0	0	1	1	0
2229	48	23	43	90254	4	1.9	3	0	0	0	1	0	0
2230	46	22	72	91711	4	1.4	2	149	0	0	1	1	0
2231	36	11	183	94704	1	3	3	0	0	1	1	1	1
2232	46	20	134	94575	1	5.7	1	146	1	0	1	0	0
2233	59	33	140	95035	2	0.5	1	262	0	0	1	0	0
2234	59	35	39	92028	1	1.8	3	0	0	0	1	0	0
2235	36	12	35	95812	4	0.4	2	0	0	0	1	0	0
2236	63	37	141	92121	2	6.9	1	0	0	0	1	1	0
2237	51	24	23	95616	1	0.5	2	0	0	0	1	0	0
2238	30	5	134	92647	1	0	1	0	0	0	1	1	0
2239	48	22	35	92709	1	1.4	3	0	0	0	1	0	0
2240	55	29	42	95833	4	2.5	1	0	1	0	0	0	0
2241	41	17	81	92868	4	0.2	3	167	1	0	0	0	0
2242	26	0	14	94301	4	0.4	1	94	0	0	1	0	0
2243	41	17	45	93437	1	1.8	1	172	1	0	1	0	0
2244	54	28	79	91342	3	1.7	2	150	0	0	1	1	0
2245	57	31	53	92806	1	0.8	2	120	0	0	0	0	0
2246	54	28	33	94111	2	0.7	2	0	0	0	0	1	0
2247	35	11	190	92093	3	3.1	2	266	0	0	0	0	1
2248	60	34	60	95616	1	2.5	3	103	0	0	0	0	0
2249	63	37	8	94618	1	0.8	2	97	0	0	1	0	0
2250	41	14	38	95814	3	1	2	150	0	0	1	0	0
2251	46	22	154	93109	1	5	1	0	0	0	1	0	0
2252	31	5	54	92173	4	2.2	2	0	0	0	0	0	0
2253	58	32	41	95819	3	1.4	1	0	0	0	1	0	0
2254	59	35	25	95827	2	0.3	1	75	0	0	1	0	0
2255	46	22	53	90025	2	1.7	1	109	0	0	0	0	0
2256	33	9	79	94612	1	0.1	1	0	0	0	1	0	0
2257	56	31	13	94305	4	0.9	2	76	0	1	1	1	0
2258	47	23	130	91763	2	1.4	1	0	0	0	0	0	0
2259	59	33	93	91320	2	0.7	2	0	0	0	0	0	0
2260	24	0	82	90401	3	0.8	1	0	0	0	1	0	0
2261	39	14	15	93561	2	0.3	2	92	0	0	0	0	0
2262	30	3	150	94305	4	5	2	0	0	0	1	0	1
2263	55	29	131	95070	2	0.7	2	0	0	0	0	1	1
2264	47	21	28	92868	3	1.5	1	0	0	0	1	0	0
2265	35	11	9	93106	4	0.7	2	0	0	0	1	0	0
2266	47	23	88	94305	4	1.4	2	0	0	0	0	0	0
2267	38	13	143	94550	1	4.1	1	0	0	0	0	0	0
2268	38	13	168	92647	2	1.3	3	0	0	0	0	0	1
2269	27	3	105	94304	1	3	2	0	1	0	0	0	1
2270	42	18	62	94305	3	2.1	3	0	0	0	1	0	0
2271	26	2	51	92103	4	2.6	1	0	0	0	1	0	0
2272	60	34	101	94928	3	4.4	1	0	0	0	1	1	0
2273	27	3	90	91365	3	0.8	1	0	0	0	1	0	0
2274	27	1	83	91775	4	2.1	3	0	0	0	1	1	0
2275	40	15	21	90034	2	0	3	0	0	0	1	0	0
2276	40	16	115	94305	1	3.4	1	0	0	0	1	0	0
2277	29	3	172	92093	4	4.4	1	0	0	0	0	0	1
2278	30	6	32	91330	2	1	2	0	0	0	0	0	0
2279	30	4	204	91107	2	4.5	1	0	0	0	1	0	0
2280	47	23	34	91711	4	0.6	1	0	0	1	1	1	0
2281	33	7	30	94920	2	2	3	132	0	0	0	0	0
2282	57	32	31	95039	3	1.3	2	0	0	0	1	1	0
2283	38	14	90	94110	2	2.7	1	0	0	0	1	1	0
2284	54	28	79	92677	4	2.6	3	0	0	0	0	0	0
2285	47	23	22	94901	4	0.6	1	0	0	0	1	1	0
2286	48	22	114	92007	1	2.4	3	0	0	0	1	0	1
2287	62	36	42	94122	1	0.5	3	128	0	0	1	0	0
2288	30	6	29	92121	1	0.2	3	90	0	0	1	0	0
2289	35	11	72	94706	3	2.6	2	0	0	0	1	0	0
2290	59	35	68	93117	1	1.8	3	95	0	0	0	0	0
2291	38	13	78	91942	4	0.7	3	0	0	0	1	0	0
2292	47	23	90	95449	1	2.7	1	323	0	0	0	0	0
2293	57	33	170	95051	2	2.1	2	0	0	0	0	0	1
2294	42	17	14	91768	2	0.1	2	0	0	0	1	0	0
2295	39	15	129	90035	2	1.9	1	0	0	0	0	0	0
2296	53	23	39	92101	3	1	3	87	0	0	1	0	0
2297	27	3	82	94305	2	0.2	1	0	0	0	0	1	0
2298	59	35	31	94063	3	0.4	2	0	0	0	1	1	0
2299	48	24	9	92630	4	0.5	2	0	0	0	0	1	0
2300	62	37	15	94583	3	0.1	3	91	0	0	0	0	0

In this example, we return all zip codes that have at least 2 records and a loan approval rate of 50% or greater:

In [1]: loan_by_zip('bank.csv',2,.5) Out[1]: ZIP Code mean size 90210 0.5 2 90254 0.5 2 91355 0.5 2 94131 0.5 2 94501 0.5 2 94704 0.5 2 94928 0.5 4 95054 1.0 2 95070 0.5 2 95211 0.5 2 95818 1.0 2

Point of Stress: This is strictly a Pandas, Series and DataFrame question (Pls no loops, no lf statements, no list comprehension). Please attach a print screen in addition to the code as evidence the scripts ran properly and as intended. Thanks.