add import statements

Author: lukebarousse

4_Problems/2_01_Pandas Accessing Data.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c330caa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a80f4fde",
@@ -107,7 +127,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_02_Pandas Data Cleaning.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0929e6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7e5d8211",
@@ -83,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_03_Pandas Data Management.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "beaec578",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5a78d60d",
@@ -83,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_04_Pandas Pivot Tables.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa26b1fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d4aeedce",
@@ -83,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_05_Pandas Index Management.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1752413",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c75c18fa",
@@ -107,7 +127,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_07_Pandas Merge DataFrames.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa1cc43b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "675a9607",
@@ -83,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_08_Pandas Concat DataFrames.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1668ec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4eba23b5",
@@ -83,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_09_Pandas Exporting Data.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56314e2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "305d444b",
@@ -83,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_10_Pandas Applying Functions.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ae69c80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3510f08a",
@@ -83,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

4_Problems/2_11_Pandas Explode.ipynb

@@ -10,6 +10,26 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3e2f3ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset\n",
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "# Loading Data\n",
+    "dataset = load_dataset('lukebarousse/data_jobs')\n",
+    "df = dataset['train'].to_pandas()\n",
+    "\n",
+    "# Data Cleanup\n",
+    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cb04c5ff",
@@ -83,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }