Fixes #206 Testing if value is an object

danfojs-browser/lib/bundle.js.LICENSE.txt

@@ -163,6 +163,23 @@
  * =============================================================================
  */
 
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
 /**
  * @license Complex.js v2.0.11 11/02/2016
  *

danfojs-browser/package.json

@@ -21,7 +21,7 @@
     "types"
   ],
   "dependencies": {
-    "@tensorflow/tfjs": "3.3.0",
+    "@tensorflow/tfjs": "3.6.0",
     "mathjs": "7.5.1",
     "table": "^5.4.6",
     "xlsx": "^0.16.7"

danfojs-browser/src/core/utils.js

@@ -34,7 +34,7 @@ export class Utils {
 
   // Returns if a value is an object
   __is_object(value) {
-    return value && typeof value === "object" && value.constructor === Object;
+    return value && typeof value === "object" && value.constructor && value.constructor.name === "Object";
   }
 
   // Returns if a value is null

danfojs-browser/yarn.lock

@@ -894,66 +894,66 @@
     readdirp "^2.2.1"
     upath "^1.1.1"
 
-"@tensorflow/tfjs-backend-cpu@3.3.0":
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-3.3.0.tgz#aa0a3ed2c6237a6e0c169678c5bd4b5a88766b1c"
-  integrity sha512-DLctv+PUZni26kQW1hq8jwQQ8u+GGc/p764WQIC4/IDagGtfGAUW1mHzWcTxtni2l4re1VrwE41ogWLhv4sGHg==
+"@tensorflow/tfjs-backend-cpu@3.6.0":
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-3.6.0.tgz#4e64a7cf1c33b203f71f8f77cd7b0ac1ef25a871"
+  integrity sha512-ZpAs17hPdKXadbtNjAsymYUILe8V7+pY4fYo8j25nfDTW/HfBpyAwsHPbMcA/n5zyJ7ZJtGKFcCUv1sl24KL1Q==
   dependencies:
     "@types/seedrandom" "2.4.27"
     seedrandom "2.4.3"
 
-"@tensorflow/tfjs-backend-webgl@3.3.0":
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-3.3.0.tgz#29dd665f6a856c9defcb9108164f845e1fdcd02e"
-  integrity sha512-GWCtXbrjPTyye3ooId9GlcNDwnIMskZarUpNIQ5g/zeISLfwEQoutA/UqJF+HzuEHgGMsWFkmaO3xKVT7UMpdg==
+"@tensorflow/tfjs-backend-webgl@3.6.0":
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-3.6.0.tgz#1ea1a73abea8d6324fd81aedf7f187ab6eb73692"
+  integrity sha512-zp7l4TmD1khgeSux/Ujaaj8M/v+e8JVIKjOci6HCGaeMNrn74lTSH9oqGPWKUCmpZME17/V0LfRHK34ddmrPSA==
   dependencies:
-    "@tensorflow/tfjs-backend-cpu" "3.3.0"
+    "@tensorflow/tfjs-backend-cpu" "3.6.0"
     "@types/offscreencanvas" "~2019.3.0"
     "@types/seedrandom" "2.4.27"
     "@types/webgl-ext" "0.0.30"
     "@types/webgl2" "0.0.5"
     seedrandom "2.4.3"
 
-"@tensorflow/tfjs-converter@3.3.0":
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-3.3.0.tgz#d9f2ffd0fbdbb47c07d5fd7c3e5dc180cff317aa"
-  integrity sha512-k57wN4yelePhmO9orcT/wzGMIuyedrMpVtg0FhxpV6BQu0+TZ/ti3W4Kb97GWJsoHKXMoing9SnioKfVnBW6hw==
+"@tensorflow/tfjs-converter@3.6.0":
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-3.6.0.tgz#32b3ff31b47e29630a82e30fbe01708facad7fd6"
+  integrity sha512-9MtatbTSvo3gpEulYI6+byTA3OeXSMT2lzyGAegXO9nMxsvjR01zBvlZ5SmsNyecNh6fMSzdL2+cCdQfQtsIBg==
 
-"@tensorflow/tfjs-core@3.3.0":
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-3.3.0.tgz#3d26bd03cb58e0ecf46c96d118c39c4a90b7f5ed"
-  integrity sha512-6G+LcCiQBl4Kza5mDbWbf8QSWBTW3l7SDjGhQzMO1ITtQatHzxkuHGHcJ4CTUJvNA0JmKf4QJWOvlFqEmxwyLQ==
+"@tensorflow/tfjs-core@3.6.0":
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-3.6.0.tgz#6b4d8175790bdff78868eabe6adc6442eb4dc276"
+  integrity sha512-bb2c3zwK4SgXZRvkTiC7EhCpWbCGp0GMd+1/3Vo2/Z54jiLB/h3sXIgHQrTNiWwhKPtst/xxA+MsslFlvD0A5w==
   dependencies:
     "@types/offscreencanvas" "~2019.3.0"
     "@types/seedrandom" "2.4.27"
     "@types/webgl-ext" "0.0.30"
     node-fetch "~2.6.1"
     seedrandom "2.4.3"
 
-"@tensorflow/tfjs-data@3.3.0":
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-data/-/tfjs-data-3.3.0.tgz#ba943bd6a486fa4cb3ca312c12646ea4dcf6cce4"
-  integrity sha512-0x28tRe6RJu5GmYq3IYN2GNnOgXU0nY+o6zZrlijkK+W3vjSTJlZzaBSifoeD6J8gzVpjs8W8qd/JKHQ1MQp8w==
+"@tensorflow/tfjs-data@3.6.0":
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-data/-/tfjs-data-3.6.0.tgz#af2f03cffb75ad8e4c2f46e192e392d9b7f977ed"
+  integrity sha512-5KU7fnU7cj/opb4aCNDoW4qma64ggDwI0PCs5KEO41T3waVHDLk6bjlFlBVRdjfZqvM0K6EfWEyoiXzdvz/Ieg==
   dependencies:
     "@types/node-fetch" "^2.1.2"
     node-fetch "~2.6.1"
 
-"@tensorflow/tfjs-layers@3.3.0":
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-3.3.0.tgz#d2097c5b22ec12e5fdbe470a88ca0a34a95ca11f"
-  integrity sha512-qO+TL2I29vWUiuFcQJXNyayWFYagwR+SIfbex8p5jjYaCGHGwE5GQcrH+ngoCgKZxm5tdMvYJsJPnih2M3fYzQ==
+"@tensorflow/tfjs-layers@3.6.0":
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-3.6.0.tgz#5358af559fc8baed304b3e567319fe93f1aa46a6"
+  integrity sha512-B7EHwAT6KFqhKzdf0e2Sr6haj9qpqpyEATV8OCPHdk+g8z2AGXOLlFfbgW6vCMjy1wb5jzYqCyZDoY3EWdgJAw==
 
-"@tensorflow/tfjs@3.3.0":
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-3.3.0.tgz#db92099dd48c0eb1c1673f705125d2b57496a1a3"
-  integrity sha512-xo22GCUCGcPtNGIdDpLPrp9ms3atXmzX8AF4y3aIBEwK5KlvGe+ZhcoQ2xEOCPQGBr7NB7AO6rwT8gRoziAHVg==
-  dependencies:
-    "@tensorflow/tfjs-backend-cpu" "3.3.0"
-    "@tensorflow/tfjs-backend-webgl" "3.3.0"
-    "@tensorflow/tfjs-converter" "3.3.0"
-    "@tensorflow/tfjs-core" "3.3.0"
-    "@tensorflow/tfjs-data" "3.3.0"
-    "@tensorflow/tfjs-layers" "3.3.0"
+"@tensorflow/tfjs@3.6.0":
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-3.6.0.tgz#e65956cd40c96523e3f5ec7a58a4bef9ef5e349c"
+  integrity sha512-uLDMDzyRkJa3fYBeR6etQTFD/t+nkQIH/DznL9hxmYoIYG8PigY2gcrc482TAvsdhiuvxCZ9rl5SyDtP93MvxQ==
+  dependencies:
+    "@tensorflow/tfjs-backend-cpu" "3.6.0"
+    "@tensorflow/tfjs-backend-webgl" "3.6.0"
+    "@tensorflow/tfjs-converter" "3.6.0"
+    "@tensorflow/tfjs-core" "3.6.0"
+    "@tensorflow/tfjs-data" "3.6.0"
+    "@tensorflow/tfjs-layers" "3.6.0"
     argparse "^1.0.10"
     chalk "^4.1.0"
     core-js "3"

danfojs-node/dist/core/frame.js

@@ -924,74 +924,34 @@ class DataFrame extends _generic.default {
   }
 
   groupby(col) {
-    let len = this.shape[0];
-    let column_names = this.column_names;
-    let col_dict = {};
-    let key_column = null;
-    let col_index = col.map(val => column_names.indexOf(val));
-    let col_dtype = this.dtypes.filter((val, index) => {
+    const len = this.shape[0];
+    const column_names = this.column_names;
+    const col_index = col.map(val => column_names.indexOf(val));
+    const col_dtype = this.dtypes.filter((val, index) => {
       return col_index.includes(index);
     });
+    const self = this;
+    const data = col.map(column_name => {
+      if (!column_names.includes(column_name)) throw new Error(`column ${column_name} does not exist`);
+      const [column_data, _] = (0, _indexing.indexLoc)(self, {
+        rows: [`0:${len}`],
+        columns: [`${column_name}`],
+        type: "loc"
+      });
+      return column_data;
+    });
+    const unique_columns = data.map(column_data => utils.__unique(column_data));
 
-    if (col.length == 2) {
-      if (column_names.includes(col[0])) {
-        var [data1, col_name1] = (0, _indexing.indexLoc)(this, {
-          rows: [`0:${len}`],
-          columns: [`${col[0]}`],
-          type: "loc"
-        });
-      } else {
-        throw new Error(`column ${col[0]} does not exist`);
-      }
-
-      if (column_names.includes(col[1])) {
-        var [data2, col_name2] = (0, _indexing.indexLoc)(this, {
-          rows: [`0:${len}`],
-          columns: [`${col[1]}`],
-          type: "loc"
-        });
-      } else {
-        throw new Error(`column ${col[1]} does not exist`);
-      }
-
-      key_column = [col[0], col[1]];
-
-      var column_1_Unique = utils.__unique(data1);
-
-      var column_2_unique = utils.__unique(data2);
-
-      for (var i = 0; i < column_1_Unique.length; i++) {
-        let col_value = column_1_Unique[i];
-        col_dict[col_value] = {};
-
-        for (var j = 0; j < column_2_unique.length; j++) {
-          let col2_value = column_2_unique[j];
-          col_dict[col_value][col2_value] = [];
-        }
-      }
-    } else {
-      if (column_names.includes(col[0])) {
-        var [data1, col_name1] = (0, _indexing.indexLoc)(this, {
-          rows: [`0:${len}`],
-          columns: [`${col[0]}`],
-          type: "loc"
-        });
-      } else {
-        throw new Error(`column ${col[0]} does not exist`);
-      }
-
-      key_column = [col[0]];
-
-      var column_Unique = utils.__unique(data1);
-
-      for (let i = 0; i < column_Unique.length; i++) {
-        let col_value = column_Unique[i];
-        col_dict[col_value] = [];
-      }
+    function getRecursiveDict(uniq_columns) {
+      const first_uniq_columns = uniq_columns[0];
+      const remaining_columns = uniq_columns.slice(1);
+      const c_dict = {};
+      if (!remaining_columns.length) first_uniq_columns.forEach(col_value => c_dict[col_value] = []);else first_uniq_columns.forEach(col_value => c_dict[col_value] = getRecursiveDict(remaining_columns));
+      return c_dict;
     }
 
-    let groups = new _groupby.GroupBy(col_dict, key_column, this.values, column_names, col_dtype).group();
-    return groups;
+    const col_dict = getRecursiveDict(unique_columns);
+    return new _groupby.GroupBy(col_dict, col, this.values, column_names, col_dtype).group();
   }
 
   column(col_name) {