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Author: krishcy25

Python-Data Combining.ipynb

@@ -1221,7 +1221,7 @@
     }
    ],
    "source": [
-    "#1.4 Combining dataframes using Merge function (Inner Join)\n",
+    "#1.3 Combining dataframes using Merge function (Inner Join)\n",
     "# This bascially is similar to inner join where the two customers who exist in Info didn't appear because they dont have transactions\n",
     "# As we have name variable in both datasets, on combining python creates automatically name_x and name_y in the final dataset\n",
     "# If we don't submit how, its inner join by default\n",
@@ -1372,7 +1372,7 @@
     }
    ],
    "source": [
-    "#1.5 Combining dataframes using Merge function (left Join)\n",
+    "#1.4 Combining dataframes using Merge function (left Join)\n",
     "# Now you can see 106 and 107 customers who doesn't have transactions\n",
     "merge_left=pd.merge(left=Info, right=Trans, how='left',on='Id')\n",
     "\n",
@@ -1521,7 +1521,7 @@
     }
    ],
    "source": [
-    "#1.6 Combining dataframes using Merge function (right Join)\n",
+    "#1.5 Combining dataframes using Merge function (right Join)\n",
     "\n",
     "merge_right=pd.merge(left=Info, right=Trans, how='right',on='Id')\n",
     "merge_right.head()"
@@ -1890,7 +1890,7 @@
     }
    ],
    "source": [
-    "#1.7 Combining dataframes using Merge function (outer Join)\n",
+    "#1.6 Combining dataframes using Merge function (outer Join)\n",
     "\n",
     "merge_outer=pd.merge(left=Info, right=Trans, how='outer',on='Id')\n",
     "\n",
@@ -2039,7 +2039,7 @@
     }
    ],
    "source": [
-    "#1.8 Use of suffixes\n",
+    "#1.7 Use of suffixes\n",
     "# If the variable exists in both datasets during merging, python creates them as var_x and var_y by default\n",
     "#However, if we want to specify the suffix explicitly- we can use the suffix option like below\n",
     "\n",
@@ -3946,7 +3946,7 @@
     }
    ],
    "source": [
-    "#4.3 Imputation of missing values with mean or any fixed value- Using fillno()\n",
+    "#4.4 Imputation of missing values with mean or any fixed value- Using fillno()\n",
     "\n",
     "mean_income= Info['Income'].mean()\n",
     "print(mean_income)\n",
@@ -3962,7 +3962,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#4.4 Dropping rows/columns which contains missing values\n",
+    "#4.5 Dropping rows/columns which contains missing values\n",
     "\n",
     "# df.dropna()- Drop rows with missing values\n",
     "# df.drop(columns_to_drop, axis=1)\n",