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

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.

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Homework Answers

Answer #1

import pandas as pd

def printme( file_name, min, perc_rate ):

bank = pd.read_csv(file_name)

bank_data = bank.groupby('ZIP Code')

filter = bank_data.filter(lambda x: len(x) > min)

print(filter[(filter["Personal Loan"] == perc_rate)])

return;

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