pandas_practice1.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating example dataframe\n",
        "np.random.seed(42)\n",
        "\n",
        "df=pd.DataFrame({\n",
        "    'ID': range(1, 101),\n",
        "    'Age': np.random.randint(18, 65, size=100),\n",
        "    'Gender': np.random.choice(['Male', 'Female'], size=100),\n",
        "    'Education': np.random.choice(['High School', 'Bachelor', 'Master', 'PhD'], size=100),\n",
        "    'Income': np.random.randint(30000, 100000, size=100),\n",
        "    'Country': np.random.choice(['USA', 'UK', 'Canada', 'Australia'], size=100),\n",
        "    'Product': np.random.choice(['A', 'B', 'C', 'D'], size=100),\n",
        "})\n",
        "df.head()"
      ],
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            "text/plain": [
              "   ID  Age  Gender    Education  Income Country Product\n",
              "0   1   56  Female  High School   64754      UK       C\n",
              "1   2   46  Female       Master   41411  Canada       B\n",
              "2   3   32    Male     Bachelor   32911      UK       A\n",
              "3   4   60    Male  High School   97270  Canada       B\n",
              "4   5   25    Male  High School   38680  Canada       A"
            ],
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          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. What is the average age of the customers in the dataset?"
      ],
      "metadata": {
        "id": "ErJMb6lD-Yw8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.Age.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwcma8EA-ZmA",
        "outputId": "1e3447e7-08c8-49a5-a6bb-87082d02045f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.88"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. What is the highest income among the customers?"
      ],
      "metadata": {
        "id": "YVA7rBCI-k6F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.Income.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fm5ia37n-nrg",
        "outputId": "7b279f29-eddb-4e42-e23e-2350ca965135"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98497"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. How many customers have a PhD degree?"
      ],
      "metadata": {
        "id": "vj3ws_T--qB1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Education']=='PhD'].shape[0]\n",
        "\n",
        "# Another method____\n",
        "\n",
        "# df.Education.value_counts().loc['PhD']\n",
        "         # CALCULATING THE FREQUENCY OF PhD degree...v_counts() returns a sub_df...so loc is a of accessing\n",
        "\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZQnIF-x-sAx",
        "outputId": "9c9ab45b-073d-4aa4-9fd3-17eea991584b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Which country has the highest number of customers?"
      ],
      "metadata": {
        "id": "RoFTXckq-u1n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.Country.value_counts().nlargest(1).index\n",
        "# value_count return sub_df, nlargest() also returns that so\n",
        "# nlargest(1).index is valid way to approach\n",
        "\n",
        "\n",
        "# Another way to solve\n",
        "\n",
        "# df.Country.value_counts().idxmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YaL2HaRH-vkO",
        "outputId": "0e4fe458-8810-434e-9189-5ad9dcea0dc6"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['UK'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. What is the most common product among the customers?"
      ],
      "metadata": {
        "id": "OaZhJFS7-zrC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Product'].value_counts()\n",
        "#df.Product.value_counts().nlargest(1).index  ___ if max has two values , then showing a overview of value_counts() is usefull..."
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "mkdIgdCC-1aX",
        "outputId": "ca3715ad-8277-4cef-dbda-563ef6710622"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    28\n",
              "D    28\n",
              "A    27\n",
              "C    17\n",
              "Name: Product, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. How many customers from Canada have an income above $60,000?"
      ],
      "metadata": {
        "id": "P9QDHFd__HUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['Income']>=60000 ) & (df['Country']=='Canada')].shape[0]\n",
        "# total values maintaining the conditions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icpNLaTyDKLq",
        "outputId": "40ed5ea4-a617-4dab-d92b-130021b83863"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "7. What is the income range (minimum and maximum) for customers with a Bachelor's degree?"
      ],
      "metadata": {
        "id": "ynCMI_le_DWI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " df[df['Education']=='Bachelor']['Income'].agg(['min','max'])\n",
        "\n",
        " # df.groupby('Education')['Income'].agg(['max','min']).loc['Bachelor']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIIfiKGVGtc2",
        "outputId": "64ad7f8a-bd07-4f95-9877-677a319fe182"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "max    96690\n",
              "min    31802\n",
              "Name: Bachelor, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8. How many customers are below the age of 30?"
      ],
      "metadata": {
        "id": "wU9-Hjah-94g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Age']<30].shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKrNbQbsJri8",
        "outputId": "c8cb3340-a3f3-40e9-902f-69b48919cb2c"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "9. How many customers with an income above $70,000 are from Australia and have a Master's degree?"
      ],
      "metadata": {
        "id": "QWx6Up1f_E2l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['Income']>70000) & (df['Country']=='Australia') & (df['Education']=='Master')].shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "do7j7Mb1KAiv",
        "outputId": "4bcbee62-fb7d-4bff-8e05-28003ca1b033"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "10. What is the average income of customers from the UK?"
      ],
      "metadata": {
        "id": "2hM7AGqV-4VL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Country']=='UK']['Income'].mean()\n",
        "\n",
        "# df.groupby('Country')['Income'].mean().loc['UK']\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-udwZfHLE5N",
        "outputId": "c59f4126-9adf-4092-b543-d59fca665a0a"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58847.166666666664"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "11. How many customers with an income above $70,000 are from Australia and have a Master's degree?"
      ],
      "metadata": {
        "id": "eP9qlYmj_AZW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['Income']>70000) & (df['Country']=='Australia') & (df['Education']=='Master')].shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "giil8WeLLWWS",
        "outputId": "58b6e6ab-ec84-4d4b-f037-9de2c4155777"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "12. What is the median age of customers with Product A?"
      ],
      "metadata": {
        "id": "dTnRk6UP-7HA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Product']=='A']['Age'].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBECLTNCPU6y",
        "outputId": "996cbac9-169d-46ff-abbb-3a89370b285d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38.0"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "13. How many customers with an income above $80,000 and aged between 30 and 40 have a PhD degree?"
      ],
      "metadata": {
        "id": "soXGRAFg-wiS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['Income']>80000) & (df['Age'].isin([30,40]))]"
      ],
      "metadata": {
        "id": "NSLzJuWv_d_V"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}