@@ -153,52 +153,52 @@ class StatisticsTests {
153
153
" yearsToRetirement" ,
154
154
)
155
155
156
- val median01 = res0[" age" ][0 ] as Int
157
- median01 shouldBe 22
156
+ val median01 = res0[" age" ][0 ] as Double
157
+ median01 shouldBe 22.0
158
158
// val median02 = res0["weight"][0] as Double
159
159
// median02 shouldBe 66.0
160
160
161
161
// scenario #1: particular column
162
162
val res1 = personsDf.groupBy(" city" ).medianFor(" age" )
163
163
res1.columnNames() shouldBe listOf (" city" , " age" )
164
164
165
- val median11 = res1[" age" ][0 ] as Int
166
- median11 shouldBe 22
165
+ val median11 = res1[" age" ][0 ] as Double
166
+ median11 shouldBe 22.0
167
167
168
168
// scenario #1.1: particular column via median
169
169
val res11 = personsDf.groupBy(" city" ).median(" age" )
170
170
res11.columnNames() shouldBe listOf (" city" , " age" )
171
171
172
- val median111 = res11[" age" ][0 ] as Int
173
- median111 shouldBe 22
172
+ val median111 = res11[" age" ][0 ] as Double
173
+ median111 shouldBe 22.0
174
174
175
175
// scenario #2: particular column with new name - schema changes
176
176
val res2 = personsDf.groupBy(" city" ).median(" age" , name = " newAge" )
177
177
res2.columnNames() shouldBe listOf (" city" , " newAge" )
178
178
179
- val median21 = res2[" newAge" ][0 ] as Int
180
- median21 shouldBe 22
179
+ val median21 = res2[" newAge" ][0 ] as Double
180
+ median21 shouldBe 22.0
181
181
182
182
// scenario #2.1: particular column with new name - schema changes but via columnSelector
183
183
val res21 = personsDf.groupBy(" city" ).median(name = " newAge" ) { " age" <Int >() }
184
184
res21.columnNames() shouldBe listOf (" city" , " newAge" )
185
185
186
- val median211 = res21[" newAge" ][0 ] as Int
187
- median211 shouldBe 22
186
+ val median211 = res21[" newAge" ][0 ] as Double
187
+ median211 shouldBe 22.0
188
188
189
189
// scenario #2.2: two columns with new name - schema changes but via columnSelector
190
190
val res22 = personsDf.groupBy(" city" ).median(name = " newAge" ) { " age" <Int >() and " yearsToRetirement" <Int >() }
191
191
res22.columnNames() shouldBe listOf (" city" , " newAge" )
192
192
193
- val median221 = res22[" newAge" ][0 ] as Int
194
- median221 shouldBe 32
193
+ val median221 = res22[" newAge" ][0 ] as Double
194
+ median221 shouldBe 32.5
195
195
196
196
// scenario #3: create new column via expression
197
197
val res3 = personsDf.groupBy(" city" ).medianOf(name = " newAge" ) { " age" <Int >() * 10 }
198
198
res3.columnNames() shouldBe listOf (" city" , " newAge" )
199
199
200
- val median31 = res3[" newAge" ][0 ] as Int
201
- median31 shouldBe 220
200
+ val median31 = res3[" newAge" ][0 ] as Double
201
+ median31 shouldBe 220.0
202
202
203
203
// scenario #3.1: create new column via expression with Double
204
204
val res31 = personsDf.groupBy(" city" ).medianOf(name = " newAge" ) { " weight" <Double >() * 10 }
0 commit comments