DataVis_Python.ipynb

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        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nattycep/FIL_Geosensing/blob/745c4ae5601a753011a3d5e1b6c6a232623a5bf7/DataVis_Python.ipynb)"
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        "# Data Import, Visualization, and Troubleshooting with Python\n",
        "\n",
        "**Expected study time :** 45 min (depending on your background)\n",
        "\n",
        "**Learning outcomes :**\n",
        "\n",
        "*Adapt an existing Python script in google colab in order to:*\n",
        "- Load data from your geosensor\n",
        "- Import data into Python\n",
        "- Visualize the data\n",
        "- Refine and export figures for presentation\n",
        "- Perform basic statistics to support a scientific finding\n",
        "\n",
        "**External References**\n",
        "- the Scipy.stat module https://docs.scipy.org/doc/scipy/reference/stats.html\n",
        "- Matplotlib cheat sheet https://github.com/matplotlib/cheatsheets\n",
        "\n"
      ]
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        "# 1.   Load Packages\n",
        "\n",
        "First step is to import Python libraries to analyse and visualise data. We will use\n",
        "\n",
        "*   NumPy - mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more\n",
        "*   MatPlotLib - comprehensive library for creating static, animated, and interactive visualizations in Python\n",
        "*   Pandas - fast, powerful, flexible and easy to use open source data analysis and manipulation tool\n",
        "*   Scipy - algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems\n",
        "*   Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
        "\n",
        "\n"
      ]
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "import seaborn as sns\n",
        "\n"
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        "# 2.   Load Data\n",
        "\n",
        "\n",
        "Make sure that your data makes sense.\n",
        "Check that the dates imported correctly.\n",
        "Look at the header and variable names.\n",
        "\n",
        "If you import from github, make sure that you are using the \"raw\" prefix after the https://   ...\n",
        "On google Colab, you also can upload local files.  To do that, click on the \"file\" icon on the left side and then the file with an arrow on it.\n",
        "\n",
        "Look at the header to make sure you data lines up correctly.   \n",
        "\n",
        "\n"
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      "source": [
        "temp = pd.read_csv('https://raw.githubusercontent.com/nattycep/FIL_Geosensing/main/temp.csv')\n",
        "temp.head()\n",
        "\n",
        "# in this data file -> each component of date was its own column, so it imported as an interger.  We need to convert it to date."
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              "   year  month  day  hour  minute  second  temperature\n",
              "0  2022      8   18    17      18      25     23.76739\n",
              "1  2022      8   18    17      18      26     26.10811\n",
              "2  2022      8   18    17      18      27     26.10811\n",
              "3  2022      8   18    17      18      28     26.10811\n",
              "4  2022      8   18    17      18      29     26.57626"
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              "summary": "{\n  \"name\": \"# in this data file -> each component of date was its own column, so it imported as an interger\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2022,\n        \"max\": 2022,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 8,\n        \"max\": 8,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 18,\n        \"max\": 18,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 17,\n        \"max\": 17,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minute\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 18,\n        \"max\": 18,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"second\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 25,\n        \"max\": 29,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1176796119550545,\n        \"min\": 23.76739,\n        \"max\": 26.57626,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          23.76739\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "Your data is now a \"data frame\".  But to make it easier to work with, let's:   \n",
        "1. Make a single data vector for each variable by extracting columns from the table"
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        "temperature = temp['temperature'].astype(int)\n",
        "#temperature = temperature.astype(int)\n",
        "print(temperature)"
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          "text": [
            "0      23\n",
            "1      26\n",
            "2      26\n",
            "3      26\n",
            "4      26\n",
            "       ..\n",
            "564    26\n",
            "565    26\n",
            "566    26\n",
            "567    26\n",
            "568    26\n",
            "Name: temperature, Length: 569, dtype: int64\n"
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        "2. Delete the successfully extracted colums\n"
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        "del temp[\"temperature\"]\n",
        "print(temp)"
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          "text": [
            "     year  month  day  hour  minute  second\n",
            "0    2022      8   18    17      18      25\n",
            "1    2022      8   18    17      18      26\n",
            "2    2022      8   18    17      18      27\n",
            "3    2022      8   18    17      18      28\n",
            "4    2022      8   18    17      18      29\n",
            "..    ...    ...  ...   ...     ...     ...\n",
            "564  2022      8   18    17      28      20\n",
            "565  2022      8   18    17      28      21\n",
            "566  2022      8   18    17      28      22\n",
            "567  2022      8   18    17      28      23\n",
            "568  2022      8   18    17      28      25\n",
            "\n",
            "[569 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Parsing the remaining columns (date and time) into a datetime vector.  This method expects columns to have headings of minimally \"year\", \"month\", day\""
      ],
      "metadata": {
        "id": "rGYSb4EcCrnF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datetime = pd.to_datetime(temp) #converting to a date time is key\n",
        "print(datetime.min()) # allows you to use special functions\n",
        "print(datetime.max())\n",
        "print(np.timedelta64(datetime.max()-datetime.min(),'m')) # declare the untis"
      ],
      "metadata": {
        "id": "lU_8Q-kgST9b",
        "outputId": "3f8608f7-8c67-417c-9ab3-731ef4e438bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-08-18 17:18:25\n",
            "2022-08-18 17:28:25\n",
            "10 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can transform this data into a time series and two value series."
      ],
      "metadata": {
        "id": "sQQ601XwMZyk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Second import example\n",
        "\n",
        "Here's an easy one -> But I still had to troubleshoot the header:"
      ],
      "metadata": {
        "id": "qmATpbvkGgcm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temps = pd.read_csv('https://raw.githubusercontent.com/nattycep/FIL_Geosensing/main/Datalog_geosensorik.csv')\n",
        "print(temps.head())\n",
        "temps_dt = temps.loc[:, [\"Year\",\t\"Month\",\t\"Day\",\t\"Hour\",\t\"Minute\",\t\"Seconds\"]]\n",
        "T_datetime = pd.to_datetime(temps_dt)\n",
        "T_ambient =temps[\"Ambient_Temperature\"]\n",
        "T_object =temps[\"Object_Temperature\"]\n",
        "\n",
        "del(temps)\n",
        "del(temps_dt)"
      ],
      "metadata": {
        "id": "x-YrHvcKK-oe",
        "outputId": "80f44c21-b582-4ce3-e19d-3fa083b45744",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Day  Month  Year  Hour  Minute  Seconds  Ambient_Temperature  \\\n",
            "0   31      8  2022    24      53       11                24.13   \n",
            "1   31      8  2022    24      53       12                24.13   \n",
            "2   31      8  2022    24      53       13                24.13   \n",
            "3   31      8  2022    24      53       15                24.11   \n",
            "4   31      8  2022    24      53       16                24.13   \n",
            "\n",
            "   Object_Temperature  \n",
            "0               22.89  \n",
            "1               22.71  \n",
            "2               22.87  \n",
            "3               22.77  \n",
            "4               22.65  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Third import example: Now let's test your test data\n",
        "\n",
        "1.  I manually uploaded the files, then right clicked to copy path\n"
      ],
      "metadata": {
        "id": "gu0PMidiSzQN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Klima1 = pd.read_csv('https://raw.githubusercontent.com/nattycep/FIL_Geosensing/main/Datalog_1%202-1.csv')\n",
        "print(Klima1.head())\n",
        "\n",
        "Klima2 = pd.read_csv('https://raw.githubusercontent.com/nattycep/FIL_Geosensing/main/Datalog_1%202.csv')\n",
        "print(Klima2.head())\n",
        "\n",
        "Klima3 = pd.read_csv('https://raw.githubusercontent.com/nattycep/FIL_Geosensing/main/Datalog_1.csv')\n",
        "print(Klima3.head())\n",
        "\n",
        "Bridge = pd.read_csv('https://raw.githubusercontent.com/nattycep/FIL_Geosensing/main/Results_OverNightTest.csv')\n",
        "#On first try, I got this error: 'utf-8' codec can't decode byte 0xb0 in position 19: invalid start byte\n",
        "# if I look, there is a funny character: Date,CO2(ppm),Temp(∞C),CarCount\n",
        "# I cheated and just changed it.\n",
        "print(Bridge.head())\n",
        "\n",
        "Water = pd.read_csv('https://raw.githubusercontent.com/nattycep/FIL_Geosensing/main/water.csv', delimiter=\";\") # on my first try, I didn't notice the deliminator was not \",\"\n",
        "print(Water.head())\n"
      ],
      "metadata": {
        "id": "9G5pvDAbTDhM",
        "outputId": "aa5fa0f4-1954-4e14-f5df-73c87d898786",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Time  Temperature in C  Humidity in %    Light\n",
            "0   11:45:5             24.59          53.73  33448.0\n",
            "1  11:45:10             24.70          53.33  31495.0\n",
            "2  11:45:15             24.77          53.75  27398.0\n",
            "3  11:45:20             24.86          53.06  32423.0\n",
            "4  11:45:25             24.97          54.03  32327.0\n",
            "       Time  Temperature in C  Humidity in %    Light\n",
            "0   11:45:5             24.59          53.73  33448.0\n",
            "1  11:45:10             24.70          53.33  31495.0\n",
            "2  11:45:15             24.77          53.75  27398.0\n",
            "3  11:45:20             24.86          53.06  32423.0\n",
            "4  11:45:25             24.97          54.03  32327.0\n",
            "       Time  Temperature in C  Humidity in %    Light\n",
            "0   11:38:2             23.56          55.07  32359.0\n",
            "1   11:38:7             23.54          54.89  32215.0\n",
            "2  11:38:12             23.53          54.91  32311.0\n",
            "3  11:38:17             23.50          54.93  32551.0\n",
            "4  11:38:22             23.47          54.96  32231.0\n",
            "               Date  CO2(ppm)  Temp(C)  CarCount\n",
            "0  01/01/2021 00:01    485.24    25.72         6\n",
            "1  01/01/2021 00:02    471.67    26.11         7\n",
            "2  01/01/2021 00:03    465.65    26.34         5\n",
            "3  01/01/2021 00:04    461.04    26.52         3\n",
            "4  01/01/2021 00:05    455.59    26.64         3\n",
            "   Day   Month   Year   Hour   Minute   Second   Temp. Air 1  Temp. Water 1  \\\n",
            "0    2       9   2022     11        2       40       20.6875           21.0   \n",
            "1    2       9   2022     11        2       43       20.6875           21.0   \n",
            "2    2       9   2022     11        2       46       20.6875           21.0   \n",
            "3    2       9   2022     11        2       49       20.6875           21.0   \n",
            "4    2       9   2022     11        2       52       20.6875           21.0   \n",
            "\n",
            "   Temp. Water 2  Temp. Water 3   \n",
            "0        20.9375         20.8125  \n",
            "1        20.9375         20.8125  \n",
            "2        20.9375         20.8125  \n",
            "3        20.9375         20.8125  \n",
            "4        20.9375         20.8125  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Water:\n",
        "1. Make the date into a date time\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "zBvnN4TxU5rj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Water.columns= Water.columns.str.lower()\n",
        "print(Water.columns) # the spaces at the beginning of the header names are causeing problems. Later the periods might also\n",
        "headers = Water.columns.str.replace(' ','')\n",
        "Water.columns = headers\n",
        "headers = Water.columns.str.replace('.','')\n",
        "Water.columns = headers\n",
        "print(Water.columns)\n",
        "\n",
        "Water['datetime']=pd.to_datetime(Water.iloc[:,[0,1,2,3,4,5]])\n",
        "\n",
        "print(Water.head())\n",
        "print(Water.dtypes)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "CCJB34SlVlcP",
        "outputId": "51633f24-0dbb-4b7f-8a18-852659fa28e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Day', ' Month', ' Year', ' Hour', ' Minute', ' Second', ' Temp. Air 1',\n",
            "       'Temp. Water 1', 'Temp. Water 2', 'Temp. Water 3 '],\n",
            "      dtype='object')\n",
            "Index(['Day', 'Month', 'Year', 'Hour', 'Minute', 'Second', 'TempAir1',\n",
            "       'TempWater1', 'TempWater2', 'TempWater3'],\n",
            "      dtype='object')\n",
            "   Day  Month  Year  Hour  Minute  Second  TempAir1  TempWater1  TempWater2  \\\n",
            "0    2      9  2022    11       2      40   20.6875        21.0     20.9375   \n",
            "1    2      9  2022    11       2      43   20.6875        21.0     20.9375   \n",
            "2    2      9  2022    11       2      46   20.6875        21.0     20.9375   \n",
            "3    2      9  2022    11       2      49   20.6875        21.0     20.9375   \n",
            "4    2      9  2022    11       2      52   20.6875        21.0     20.9375   \n",
            "\n",
            "   TempWater3            datetime  \n",
            "0     20.8125 2022-09-02 11:02:40  \n",
            "1     20.8125 2022-09-02 11:02:43  \n",
            "2     20.8125 2022-09-02 11:02:46  \n",
            "3     20.8125 2022-09-02 11:02:49  \n",
            "4     20.8125 2022-09-02 11:02:52  \n",
            "Day                    int64\n",
            "Month                  int64\n",
            "Year                   int64\n",
            "Hour                   int64\n",
            "Minute                 int64\n",
            "Second                 int64\n",
            "TempAir1             float64\n",
            "TempWater1           float64\n",
            "TempWater2           float64\n",
            "TempWater3           float64\n",
            "datetime      datetime64[ns]\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All:\n",
        "1. Check that the date is correct\n",
        "- see that bridge starts 1/1/2021\n"
      ],
      "metadata": {
        "id": "b3tR4dL8j6vA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(Bridge.dtypes)\n",
        "Bridge['datetime']=pd.to_datetime(Bridge.Date)\n",
        "print(Bridge.head())"
      ],
      "metadata": {
        "id": "aiTOe2gWjy19",
        "outputId": "bb0463cf-0d74-4fc4-f09c-d065279b1e78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date         object\n",
            "CO2(ppm)    float64\n",
            "Temp(C)     float64\n",
            "CarCount      int64\n",
            "dtype: object\n",
            "               Date  CO2(ppm)  Temp(C)  CarCount            datetime\n",
            "0  01/01/2021 00:01    485.24    25.72         6 2021-01-01 00:01:00\n",
            "1  01/01/2021 00:02    471.67    26.11         7 2021-01-01 00:02:00\n",
            "2  01/01/2021 00:03    465.65    26.34         5 2021-01-01 00:03:00\n",
            "3  01/01/2021 00:04    461.04    26.52         3 2021-01-01 00:04:00\n",
            "4  01/01/2021 00:05    455.59    26.64         3 2021-01-01 00:05:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- no date for Klima data\n",
        "\n",
        "\n",
        "Klima:\n",
        "\n",
        "1.   Merge the 3 data sources according to time\n"
      ],
      "metadata": {
        "id": "nDiB9TcAj9Jc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#print(Klima1.dtypes)\n",
        "Klima1['datetime']=pd.to_datetime(Klima1.Time) # assumed that it was today\n",
        "#print(Klima1.dtypes)\n",
        "#print(Klima1.head())\n",
        "Klima2['datetime']=pd.to_datetime(Klima2.Time) # assumed that it was today\n",
        "Klima3['datetime']=pd.to_datetime(Klima3.Time) # assumed that it was today\n",
        "\n",
        "# to merge,first need unique column names\n",
        "\n",
        "#print(Klima1.columns)\n",
        "#print(Klima2.columns)\n",
        "#print(Klima3.columns)\n",
        "\n",
        "\n",
        "Klima1.rename(columns={'Time1':'Time'}, inplace=True)\n",
        "Klima1.rename(columns={'Temp1':'Temperature in C'}, inplace=True)\n",
        "Klima1.rename(columns={'Humid1':'Humidity in %'}, inplace=True)\n",
        "Klima1.rename(columns={'Light1':'Light'}, inplace=True)\n",
        "Klima1.rename(columns={'datetime1':'datetime'}, inplace=True)\n",
        "\n",
        "\n",
        "Klima2.rename(columns={'Time2':'Time'}, inplace=True)\n",
        "Klima2.rename(columns={'Temp2':'Temperature in C'}, inplace=True)\n",
        "Klima2.rename(columns={'Humid2':'Humidity in %'}, inplace=True)\n",
        "Klima2.rename(columns={'Light2':'Light'}, inplace=True)\n",
        "Klima2.rename(columns={'datetime2':'datetime'}, inplace=True)\n",
        "\n",
        "Klima3.rename(columns={'Time3':'Time'}, inplace=True)\n",
        "Klima3.rename(columns={'Temp3':'Temperature in C'}, inplace=True)\n",
        "Klima3.rename(columns={'Humid3':'Humidity in %'}, inplace=True)\n",
        "Klima3.rename(columns={'Light3':'Light'}, inplace=True)\n",
        "Klima3.rename(columns={'datetime3':'datetime'}, inplace=True)\n",
        "\n",
        "Klima12 = pd.merge(Klima1,Klima2, on='datetime', how='outer').sort_values(by='datetime').fillna(method='ffill')\n",
        "Klima = pd.merge(Klima12,Klima3, on='datetime', how='outer').sort_values(by='datetime').fillna(method='ffill')\n",
        "\n",
        "print(Klima.head())\n",
        "\n",
        "del Klima1\n",
        "del Klima2\n",
        "del Klima3\n",
        "del Klima12"
      ],
      "metadata": {
        "id": "CFe0-L8oj_BX",
        "outputId": "80c9cd05-e80d-496e-b01a-c9999aa732ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Time_x  Temperature in C_x  Humidity in %_x  Light_x            datetime  \\\n",
            "10    NaN                 NaN              NaN      NaN 2024-08-21 11:38:02   \n",
            "11    NaN                 NaN              NaN      NaN 2024-08-21 11:38:07   \n",
            "12    NaN                 NaN              NaN      NaN 2024-08-21 11:38:12   \n",
            "13    NaN                 NaN              NaN      NaN 2024-08-21 11:38:17   \n",
            "14    NaN                 NaN              NaN      NaN 2024-08-21 11:38:22   \n",
            "\n",
            "   Time_y  Temperature in C_y  Humidity in %_y  Light_y      Time  \\\n",
            "10    NaN                 NaN              NaN      NaN   11:38:2   \n",
            "11    NaN                 NaN              NaN      NaN   11:38:7   \n",
            "12    NaN                 NaN              NaN      NaN  11:38:12   \n",
            "13    NaN                 NaN              NaN      NaN  11:38:17   \n",
            "14    NaN                 NaN              NaN      NaN  11:38:22   \n",
            "\n",
            "    Temperature in C  Humidity in %    Light  \n",
            "10             23.56          55.07  32359.0  \n",
            "11             23.54          54.89  32215.0  \n",
            "12             23.53          54.91  32311.0  \n",
            "13             23.50          54.93  32551.0  \n",
            "14             23.47          54.96  32231.0  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-bf4854c32293>:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  Klima1['datetime']=pd.to_datetime(Klima1.Time) # assumed that it was today\n",
            "<ipython-input-34-bf4854c32293>:5: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  Klima2['datetime']=pd.to_datetime(Klima2.Time) # assumed that it was today\n",
            "<ipython-input-34-bf4854c32293>:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  Klima3['datetime']=pd.to_datetime(Klima3.Time) # assumed that it was today\n",
            "<ipython-input-34-bf4854c32293>:34: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
            "  Klima12 = pd.merge(Klima1,Klima2, on='datetime', how='outer').sort_values(by='datetime').fillna(method='ffill')\n",
            "<ipython-input-34-bf4854c32293>:35: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
            "  Klima = pd.merge(Klima12,Klima3, on='datetime', how='outer').sort_values(by='datetime').fillna(method='ffill')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fourth import example\n",
        "\n",
        "For our example plots, let's work with some meteo suisse data from this weekend.  \n",
        "\n",
        "*   ten-minute resolution\n",
        "*   two stations:\n",
        "    *     NABBER    Bern / Bollwerk\n",
        "    *     BER       Bern / Zollikofen\n",
        "*   two variables\n",
        "    *     tre200s0,  ∞C Air temperature 2 m above ground; current value\n",
        "    *     rre150z0, mm  Precipitation; ten minutes total\n",
        "\n"
      ],
      "metadata": {
        "id": "942q2CKgVlAO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"https://raw.githubusercontent.com/nattycep/FIL_Geosensing/main/order_105725_data.txt\"\n",
        "ms = pd.read_csv(path, delimiter= \";\")\n",
        "ms.time= pd.to_datetime(ms['time'].astype(str),errors='raise',format='%Y%m%d%H%M')\n",
        "ms.stn = ms['stn'].astype('category')\n",
        "print(ms.head())\n",
        "print(ms.dtypes)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "xi1ZqyTaamN8",
        "outputId": "69c3e877-046b-45c1-af50-a1cab5a5e245",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      stn                time  tre200s0  rre150z0\n",
            "0  NABBER 2022-09-01 00:00:00      15.3       0.0\n",
            "1  NABBER 2022-09-01 00:10:00      15.3       0.0\n",
            "2  NABBER 2022-09-01 00:20:00      15.3       0.0\n",
            "3  NABBER 2022-09-01 00:30:00      15.3       0.0\n",
            "4  NABBER 2022-09-01 00:40:00      15.3       0.0\n",
            "stn               category\n",
            "time        datetime64[ns]\n",
            "tre200s0           float64\n",
            "rre150z0           float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Maybe we want to pivot this, to make a single row for each time\n"
      ],
      "metadata": {
        "id": "Yy326tyII7HH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temperature = ms.pivot(index='time', columns='stn', values='tre200s0')\n",
        "rain = ms.pivot(index='time', columns='stn', values='rre150z0')\n",
        "\n",
        "print(temperature.head())\n",
        "print(rain.head())\n"
      ],
      "metadata": {
        "id": "3lB7sLuBJAku",
        "outputId": "7cf3c5e5-a2ed-4219-c1dd-3e78822584aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "stn                   BER  NABBER\n",
            "time                             \n",
            "2022-09-01 00:00:00  14.2    15.3\n",
            "2022-09-01 00:10:00  14.0    15.3\n",
            "2022-09-01 00:20:00  13.8    15.3\n",
            "2022-09-01 00:30:00  13.9    15.3\n",
            "2022-09-01 00:40:00  13.7    15.3\n",
            "stn                  BER  NABBER\n",
            "time                            \n",
            "2022-09-01 00:00:00  0.0     0.0\n",
            "2022-09-01 00:10:00  0.0     0.0\n",
            "2022-09-01 00:20:00  0.0     0.0\n",
            "2022-09-01 00:30:00  0.0     0.0\n",
            "2022-09-01 00:40:00  0.0     0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.   Make Figures: Preliminary, Refine, Export\n",
        "\n",
        "Start with looking at each variable independently.  For example, a histogram of one variable and a line of another.\n",
        "\n",
        "Remember to always label your axes.\n",
        "\n"
      ],
      "metadata": {
        "id": "_HmhpySYFE9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(temperature.BER)\n",
        "plt.ylabel(\"$ Temperature [^OC]$\")\n",
        "plt.xlabel(\"datetime\")"
      ],
      "metadata": {
        "id": "Z7rI-LwuFMrW",
        "outputId": "fdb3bcb3-e4d1-4c46-ec1c-5179278f5b01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'datetime')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histogram\n",
        "\n",
        "A histogram is the most useful plot for seeing the first distribution of the data. Here are two different ways to visualize the histogram, for our two different time series.\n",
        "\n",
        "Note: to get superscript in matplotlib axes labels, use dollar signs that signal equation mode around a carrot (^). Subscripts would be around an underscore (_), and greek similarly need dollar signs but then a back slash followed by the name of the letter (capitalized for the capital version).  i.e.:\n",
        "```\n",
        "plt.ylabel(\"$ \\Delta _{temperature} [^OC]$\")\n",
        "```\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "y2SfmZ0YFjPB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(temperature.BER)\n",
        "plt.xlabel(\"frequency\")\n",
        "plt.ylabel(\" temperature [$^OC$]\")"
      ],
      "metadata": {
        "id": "PmAQ95OuFi2i",
        "outputId": "44e2ee65-efbe-4d5a-a2bf-3f27c18ae3d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, ' temperature [$^OC$]')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Or basically the same, just called a little differently:"
      ],
      "metadata": {
        "id": "4XVQWf2sLiEe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.hist(temperature.BER)\n",
        "plt.ylabel(\"frequency\")\n",
        "plt.xlabel(\"$temperature ^OC$\")\n"
      ],
      "metadata": {
        "id": "4PIvXfNKFinL",
        "outputId": "e1ec8399-3629-4c4a-c3ab-59c83c52c2b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, '$temperature ^OC$')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Or if there are two variables, we can see how their distributions compare."
      ],
      "metadata": {
        "id": "v8bhDP1bDemF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.hist(temperature.BER)\n",
        "ax.hist(temperature.NABBER)\n",
        "plt.ylabel(\"frequency\")\n",
        "plt.xlabel(\"$temperature ^OC$\")\n",
        "ax.legend(labels = {'Zollikofen', 'Bollwerk'})\n",
        "\n",
        "#NABBER Bern / Bollwerk\n",
        "#BER Bern / Zollikofen\n",
        "\n",
        "#plt.hist(norm.rvs(0,1,5000))  # depending on your bins, you see differnet structure. how to decide on binning?\n",
        "# doesn't make sense to have bins that are smaller than the accuracy of your data, i.e. 1 cm.\n",
        "# let python decide\n",
        "# plot with correct uncertainty bars (the best)\n",
        "#plt.hist(norm.rvs(0,1,5000), bins=15)  # depending on your bins, you see differnet structure. how to decide on binning?"
      ],
      "metadata": {
        "id": "d5HR6mqtDlIT",
        "outputId": "eb6396ce-ab71-459d-fa72-2eb9b2c877ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x78e04ea3dff0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Maybe we want to make them transparent"
      ],
      "metadata": {
        "id": "Ls4ZhSEsdWMf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "n, bins, patches = ax.hist(temperature.BER, alpha=.5, edgecolor='white') # so that we can match the bins\n",
        "print(bins)\n",
        "ax.hist(temperature.NABBER, alpha=.5, bins = bins, edgecolor='white') # here we use the same bins\n",
        "plt.ylabel(\"frequency\")\n",
        "plt.xlabel(\"$temperature ^OC$\")\n",
        "ax.legend(labels = {'Zollikofen', 'Bollwerk'})\n",
        "\n",
        "print(temperature.BER.mean())\n",
        "print(temperature.NABBER.mean())\n"
      ],
      "metadata": {
        "id": "rSWBAELfGIwW",
        "outputId": "2171b68d-b323-4bf0-c0db-4966bfd5d5ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 9.3  10.72 12.14 13.56 14.98 16.4  17.82 19.24 20.66 22.08 23.5 ]\n",
            "16.496304347826086\n",
            "17.32217391304348\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Time Series"
      ],
      "metadata": {
        "id": "q4uON3btHYgz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(temperature.BER)\n",
        "ax.plot(temperature.NABBER)\n",
        "ax.legend(labels = {'Zollikofen', 'Bollwerk'})\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "mjJpLAu7HXih",
        "outputId": "1a25d0ac-84ed-4a06-d682-cd465b1ba4a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Change the marker"
      ],
      "metadata": {
        "id": "nvEVxeX5HkcU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(temperature.BER, 'o')\n",
        "fig.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ACEPEYvCHm0c",
        "outputId": "1d3733fb-95fd-41d5-bb3c-6684f56c8acf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perhaps we want to add a bined function to this figure and make the original data transparent. For this, use grouupby. Another option would be to use scipy binned statistic"
      ],
      "metadata": {
        "id": "DjFOX2yVDXuY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# note in this concatted data, the index is the time, but in your data it might be a different column, or another variable.\n",
        "\n",
        "temperature.index.min()\n",
        "temperature.index.max()\n",
        "\n",
        "temp6=temperature.groupby(temperature.index.floor('6H')).mean() #anotheroption would 30min or 1D (day)\n",
        "temp6s=temperature.groupby(temperature.index.floor('6H')).std()"
      ],
      "metadata": {
        "id": "tPg-yJ28OFRv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot these resampled values and add an error bar."
      ],
      "metadata": {
        "id": "7k1sSNU0ZS11"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(temperature.BER, 'ro', alpha=0.1)\n",
        "ax.plot(temp6.BER, 'bo-');\n",
        "ax.errorbar(x=temp6.index, y=temp6.BER, yerr=temp6s.BER/2, color='b', capsize=3);  # add error bars\n",
        "\n",
        "fig.show()\n",
        "\n",
        "\n",
        "# note at the 6 hour time step, this isn't so nice.  We really should shift the dates by 3 hours so that the mean points are in the middle."
      ],
      "metadata": {
        "id": "mL2k32AnKXVc",
        "outputId": "de5a5628-293d-445d-cadc-6f12a03ae917",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit a function\n",
        "\n",
        "Perhaps instead of a moving average, we want to fit a function to the data.\n",
        "\n",
        "First a linear function.\n",
        "\n",
        "Let's start by changing our x-variable to a deltatime"
      ],
      "metadata": {
        "id": "weh-8JYwf9Y3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt = temperature.index -temperature.index.min()\n",
        "dtemp = temperature\n",
        "dtemp.index = dt\n",
        "print(dtemp.head())"
      ],
      "metadata": {
        "id": "U-BvOh7VaG0E",
        "outputId": "c7bb952e-de4a-44dd-f519-17a576d5e7c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "stn               BER  NABBER\n",
            "time                         \n",
            "0 days 00:00:00  14.2    15.3\n",
            "0 days 00:10:00  14.0    15.3\n",
            "0 days 00:20:00  13.8    15.3\n",
            "0 days 00:30:00  13.9    15.3\n",
            "0 days 00:40:00  13.7    15.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#linear fit\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "plt.ylabel(\"$Bollwerk_{temperature} [^OC]$\")\n",
        "plt.xlabel(\"$Zollikofen_{temperature} [^OC]$\")\n",
        "p= sns.regplot(data=dtemp, x=dtemp.NABBER.values, y=dtemp.BER.values);\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "XzYWssQHuZgw",
        "outputId": "9d09900c-110e-4f78-8d44-d76872287568",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that regplot makes a it a bit tricky to get the equations, but you can figure it out"
      ],
      "metadata": {
        "id": "T0X6_EQeeUZj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "slope, intercept, r_value, p_value, std_err = stats.linregress(x=p.get_lines()[0].get_xdata(),y=p.get_lines()[0].get_ydata())\n",
        "\n",
        "print(slope)\n",
        "print(intercept)\n",
        "print(r_value)\n",
        "print(p_value)\n",
        "print(std_err)\n"
      ],
      "metadata": {
        "id": "fLejfRVxeiAp",
        "outputId": "9f6fdad3-36b4-48ba-9671-e50dd63925dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.114649917051953\n",
            "-2.8118553675073237\n",
            "0.9999999999999999\n",
            "0.0\n",
            "1.6778207570239026e-09\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We could make this more interesting by seeing if the relationship varies according to a categorical variable, say if it is raining in either place."
      ],
      "metadata": {
        "id": "DtJ0DFnWdoYm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "israining = (rain.BER+rain.NABBER)>0\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "plt.ylabel(\"$Bollwerk_{temperature} [^OC]$\")\n",
        "plt.xlabel(\"$Zollikofen_{temperature} [^OC]$\")\n",
        "p= sns.scatterplot(data=dtemp, x=dtemp.NABBER.values, y=dtemp.BER.values, hue =israining);\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "bIRJU3DZd1Vi",
        "outputId": "0ce13881-d517-4c13-e010-dc74c8afc631",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boxplots\n"
      ],
      "metadata": {
        "id": "uBKYiKlUWuhr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot([dtemp.BER, dtemp.NABBER])\n",
        "plt.ylabel(\"temperature ^OC\")\n",
        "locs, labels = plt.xticks()  # Get the current locations and labels.\n",
        "print(locs)\n",
        "plt.xticks(locs, ['Zolli', 'Bollw'],rotation=45)  # Set text labels and properties.\n",
        "fig.show()\n"
      ],
      "metadata": {
        "id": "47wc06X1ukQz",
        "outputId": "dac7348b-9022-4bf2-8ef1-f7788b87e55c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save your figure"
      ],
      "metadata": {
        "id": "5iCwU8rpjClG"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h20muCtpH1b5"
      },
      "source": [
        "fig.savefig(\"mydatavis.png\", dpi=300)\n",
        "\n",
        "fig.savefig(\"mydavavis.pdf\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3A8he82uJo4d"
      },
      "source": [
        "\n",
        "# 4.  Perform Statistical Tests\n",
        "\n",
        "Start with the visualisations and plots above:\n",
        "\n",
        "\n",
        "\n",
        "1.   Averages and standard deviations of binned data\n",
        "2.   Extract fit characteristics of regressions\n",
        "3.   Compare the box plots\n",
        "4.   Look at the normalacy of the histograms\n",
        "5.   but then there is a lot more ....\n",
        "\n",
        "## Look at the Residuals\n",
        "\n",
        "Whenver you do a fit of the data, it's a good idea to check the residuals, i.e. the difference between actual and predicted data, which ideally should be distributed around 0. Seaborn has a specific function for that called residplot.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "de2rVKG7JrXX",
        "outputId": "d09f622c-c864-4f47-f823-9b4d10d886f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "sns.residplot(data=dtemp, x=dtemp.NABBER.values, y=dtemp.BER.values);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are so many different tests to explore, but here is one that might be useful\n",
        "\n",
        "## Unpaired t test for normally distributed data assuming equal variance\n"
      ],
      "metadata": {
        "id": "cwOhn9zbvaxp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(dtemp.NABBER.values, dtemp.BER.values, equal_var = True) # True means we assume equal variances\n",
        "\n"
      ],
      "metadata": {
        "id": "Coq81dRhvlZF",
        "outputId": "2a9619ef-56a8-4983-fba5-925aaa49e773",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=4.19799747423107, pvalue=2.9548180993260417e-05, df=918.0)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interpretation : It is extremly unlikely that these samples would come from a population with the same mean and the same variance.\n",
        "The p-value got slightly worse, but such small p-values have no meaning anyway. p-values below 0.01 are often not that precise. We just know they are below 0.01.\n",
        "\n",
        "Let's calculate the mean again just to visualize:"
      ],
      "metadata": {
        "id": "94QldONAv6Yi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"%1.2f %1.2f\" % (dtemp.NABBER.mean(), dtemp.BER.mean()))\n",
        "\n",
        "print(\"%1.2f %1.2f\" % (dtemp.NABBER.std(), dtemp.BER.std()))"
      ],
      "metadata": {
        "id": "NXR8VSGEwgTk",
        "outputId": "1ea5a082-748a-48f4-b8ae-8448ec9dbea0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17.32 16.50\n",
            "2.75 3.20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Save the data after you've worked with it"
      ],
      "metadata": {
        "id": "AObYRB1xZPv8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temperature.to_csv('export_test.csv', index=False, sep=';')"
      ],
      "metadata": {
        "id": "y1xP5gcyZTAn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}