DoubleColumn.set only replaces first 0 when using isEqualTo(0.0)
See original GitHub issueWhile following the plotting examples I did the following (in Scala):
val property: Table = Table.read().csv("./data/tablesaw/sacramento_real_estate_transactions.csv")
val sqft: DoubleColumn = property.numberColumn("sq__ft").asDoubleColumn()
//sqft.set(sqft.isEqualTo(0.0), java.lang.Double.NaN)
sqft.set(sqft.isEqualTo(0.0), -9999.0)
println("Unique +++++++++++++++++++++++++++")
val tmp = sqft.unique()
tmp.sortAscending()
println(tmp.asObjectArray().mkString(","))
val histogram2 = Histogram.create("Distribution of property sizes", property, "sq__ft")
Plot.show( histogram2 )
I noticed that the plot still had values in the 0-199 range (unlike the guide’s output). To check this I looked at the CSV file and confirmed the 0
were exact. I also print the data of the unique values but it shows (see below) shows that the 0.0
are repeated. The repeats are also an issue and are reported in #586.
However as seen below the set
methods only seems to substitute the first 0 value.
Unique +++++++++++++++++++++++++++
-9999.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,484.0,539.0,588.0,610.0,611.0,623.0,625.0,682.0,696.0,722.0,723.0,746.0,756.0,760.0,779.0,780.0,795.0,796.0,797.0,800.0,804.0,810.0,832.0,834.0,836.0,838.0,840.0,844.0,846.0,852.0,861.0,864.0,868.0,871.0,876.0,881.0,884.0,888.0,900.0,901.0,904.0,906.0,909.0,911.0,918.0,924.0,930.0,932.0,933.0,936.0,941.0,948.0,950.0,952.0,956.0,957.0,958.0,960.0,962.0,963.0,964.0,966.0,967.0,970.0,972.0,975.0,984.0,988.0,990.0,994.0,998.0,1000.0,1004.0,1006.0,1007.0,1009.0,1010.0,1011.0,1012.0,1013.0,1014.0,1018.0,1019.0,1020.0,1022.0,1029.0,1032.0,1039.0,1040.0,1043.0,1050.0,1051.0,1056.0,1058.0,1060.0,1064.0,1067.0,1073.0,1074.0,1075.0,1080.0,1082.0,1088.0,1089.0,1092.0,1093.0,1094.0,1098.0,1099.0,1100.0,1103.0,1104.0,1108.0,1110.0,1112.0,1115.0,1116.0,1117.0,1118.0,1119.0,1120.0,1122.0,1123.0,1124.0,1126.0,1127.0,1130.0,1132.0,1134.0,1137.0,1139.0,1140.0,1144.0,1146.0,1150.0,1152.0,1154.0,1156.0,1157.0,1158.0,1159.0,1160.0,1161.0,1162.0,1164.0,1166.0,1167.0,1169.0,1170.0,1172.0,1174.0,1175.0,1176.0,1177.0,1179.0,1182.0,1183.0,1185.0,1187.0,1188.0,1189.0,1193.0,1196.0,1199.0,1200.0,1202.0,1204.0,1206.0,1207.0,1209.0,1211.0,1212.0,1215.0,1216.0,1217.0,1219.0,1220.0,1229.0,1231.0,1232.0,1235.0,1240.0,1245.0,1247.0,1248.0,1249.0,1250.0,1252.0,1253.0,1255.0,1258.0,1260.0,1261.0,1262.0,1264.0,1265.0,1266.0,1269.0,1272.0,1273.0,1276.0,1277.0,1280.0,1284.0,1285.0,1287.0,1289.0,1291.0,1292.0,1294.0,1296.0,1300.0,1302.0,1304.0,1305.0,1306.0,1309.0,1310.0,1315.0,1316.0,1317.0,1319.0,1320.0,1321.0,1324.0,1326.0,1327.0,1328.0,1329.0,1331.0,1337.0,1338.0,1340.0,1341.0,1343.0,1344.0,1348.0,1351.0,1353.0,1354.0,1356.0,1358.0,1360.0,1362.0,1364.0,1366.0,1367.0,1370.0,1371.0,1373.0,1375.0,1376.0,1380.0,1381.0,1382.0,1386.0,1390.0,1392.0,1393.0,1394.0,1400.0,1401.0,1404.0,1406.0,1407.0,1410.0,1411.0,1416.0,1418.0,1419.0,1420.0,1424.0,1427.0,1428.0,1430.0,1436.0,1438.0,1439.0,1440.0,1441.0,1443.0,1446.0,1448.0,1449.0,1450.0,1451.0,1452.0,1456.0,1457.0,1462.0,1463.0,1465.0,1466.0,1468.0,1472.0,1473.0,1475.0,1477.0,1478.0,1479.0,1483.0,1485.0,1488.0,1493.0,1497.0,1498.0,1502.0,1503.0,1505.0,1506.0,1510.0,1511.0,1512.0,1513.0,1516.0,1517.0,1520.0,1522.0,1524.0,1527.0,1529.0,1531.0,1540.0,1541.0,1543.0,1548.0,1549.0,1555.0,1559.0,1566.0,1567.0,1570.0,1574.0,1577.0,1578.0,1580.0,1582.0,1586.0,1587.0,1590.0,1591.0,1593.0,1595.0,1596.0,1598.0,1601.0,1603.0,1605.0,1606.0,1608.0,1609.0,1616.0,1621.0,1623.0,1624.0,1627.0,1628.0,1636.0,1637.0,1638.0,1639.0,1643.0,1646.0,1650.0,1653.0,1655.0,1656.0,1659.0,1669.0,1670.0,1671.0,1672.0,1676.0,1677.0,1678.0,1683.0,1685.0,1686.0,1690.0,1691.0,1695.0,1697.0,1711.0,1712.0,1713.0,1714.0,1715.0,1716.0,1718.0,1720.0,1721.0,1724.0,1727.0,1735.0,1736.0,1739.0,1740.0,1744.0,1751.0,1758.0,1766.0,1768.0,1770.0,1776.0,1788.0,1789.0,1795.0,1798.0,1799.0,1800.0,1801.0,1808.0,1810.0,1811.0,1816.0,1820.0,1828.0,1829.0,1830.0,1838.0,1843.0,1844.0,1851.0,1856.0,1857.0,1859.0,1871.0,1872.0,1876.0,1882.0,1887.0,1888.0,1890.0,1892.0,1899.0,1900.0,1904.0,1905.0,1910.0,1914.0,1915.0,1917.0,1921.0,1922.0,1928.0,1929.0,1936.0,1939.0,1940.0,1943.0,1953.0,1955.0,1961.0,1962.0,1968.0,1981.0,1982.0,1991.0,1993.0,1995.0,2002.0,2004.0,2016.0,2025.0,2026.0,2030.0,2049.0,2052.0,2053.0,2054.0,2056.0,2068.0,2085.0,2093.0,2096.0,2100.0,2109.0,2110.0,2111.0,2126.0,2136.0,2142.0,2155.0,2159.0,2160.0,2161.0,2162.0,2163.0,2166.0,2169.0,2170.0,2172.0,2175.0,2185.0,2187.0,2199.0,2205.0,2208.0,2212.0,2213.0,2214.0,2218.0,2222.0,2235.0,2254.0,2258.0,2274.0,2278.0,2280.0,2295.0,2306.0,2307.0,2309.0,2325.0,2329.0,2334.0,2346.0,2347.0,2354.0,2356.0,2359.0,2367.0,2372.0,2379.0,2382.0,2442.0,2447.0,2457.0,2462.0,2475.0,2484.0,2491.0,2494.0,2503.0,2504.0,2508.0,2550.0,2555.0,2575.0,2577.0,2581.0,2592.0,2605.0,2606.0,2607.0,2652.0,2660.0,2687.0,2711.0,2724.0,2725.0,2734.0,2787.0,2789.0,2790.0,2800.0,2824.0
I assume all 0
should be cahnged. Am I correct?
Using version 0.34.2
Issue Analytics
- State:
- Created 4 years ago
- Comments:10
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Top GitHub Comments
Regarding the docs, there are some alternatives we’re considering here: https://github.com/jtablesaw/tablesaw/issues/457
Don’t apologize. You have done a great job. Set your priorities and let others contribute.
As for the documentation I have 2 projects with Scala and use MDoc and Tut respectively to process code snippets in Markdown. This has 2 advantages: a) you just copy and paste test cases to the markdown files to generate the examples and b) the code snippets will be automatically checked when compiled so its easier to maintain. Unfortunately I have not found a Java version of these. May someone else knows?
As I said, the plotting capabilities is a big plus.
Ok. Not an issue for me now. So low priority.
Thanks.