23040.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2u-37yAJ0LZv","executionInfo":{"status":"ok","timestamp":1677868812739,"user_tz":300,"elapsed":22006,"user":{"displayName":"Alexander Grande","userId":"11675376131495635883"}},"outputId":"fa2fc1a3-53f0-405d-a8d9-49753f3273ae"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":[],"metadata":{"id":"QyYVzl-gL29G"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","df = pd.read_csv('/content/drive/MyDrive/KC Survey_All Years.csv')\n","df\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"VUWanUWG3ot-","executionInfo":{"status":"ok","timestamp":1677868813547,"user_tz":300,"elapsed":813,"user":{"displayName":"Alexander Grande","userId":"11675376131495635883"}},"outputId":"ff3a418e-106d-4bb6-afb3-059fb5de9dea"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        zip  work_outside_home  Commute  beh_read  beh_type  beh_handheld  \\\n","0     98001                  1      1.0         2         2             3   \n","1     98052                  1      1.0         5         5             5   \n","2     98042                  1      1.0         5         5             2   \n","3     98028                  2      NaN         5         5             5   \n","4     98003                  2      NaN         5         5             5   \n","...     ...                ...      ...       ...       ...           ...   \n","3838  98122                  2      NaN         5         5             5   \n","3839  98033                  1      1.0         5         5             5   \n","3840  98108                  1      1.0         3         3             5   \n","3841  98058                  1      1.0         3         2             1   \n","3842  98006                  2      NaN         4         5             2   \n","\n","      beh_handfree  beh_app  acc_handheld  acc_handfree  ...  drive30days  \\\n","0                5        3             3             2  ...            1   \n","1                2        3             4             4  ...            2   \n","2                2        5             4             2  ...            1   \n","3                5        5             4             1  ...            2   \n","4                3        5             3             3  ...            1   \n","...            ...      ...           ...           ...  ...          ...   \n","3838             5        5             4             2  ...            2   \n","3839             5        5             4             2  ...            1   \n","3840             3        5             4             2  ...            1   \n","3841             1        2             2             1  ...            1   \n","3842             3        3             3             3  ...            1   \n","\n","      Urbanicity    weight  Language  legal_gps2122  legal_music2122  \\\n","0            2.0  0.611521       NaN            NaN              NaN   \n","1            2.0  1.077168       NaN            NaN              NaN   \n","2            2.0  1.459666       NaN            NaN              NaN   \n","3            2.0  0.602725       NaN            NaN              NaN   \n","4            2.0  0.498099       NaN            NaN              NaN   \n","...          ...       ...       ...            ...              ...   \n","3838         1.0  0.799188       1.0            2.0              2.0   \n","3839         2.0  1.024891       1.0            3.0              3.0   \n","3840         1.0  0.459909       1.0            1.0              1.0   \n","3841         1.0  0.664518       1.0            1.0              2.0   \n","3842         2.0  2.430932       1.0            1.0              1.0   \n","\n","      legal_app2122  year  month  day  \n","0               NaN  2018      6   17  \n","1               NaN  2018      6   17  \n","2               NaN  2018      6   17  \n","3               NaN  2018      6   17  \n","4               NaN  2018      6   17  \n","...             ...   ...    ...  ...  \n","3838            2.0  2022      4   30  \n","3839            3.0  2022      4   30  \n","3840            2.0  2022      4   30  \n","3841            1.0  2022      4   30  \n","3842            2.0  2022      4   30  \n","\n","[3843 rows x 72 columns]"],"text/html":["\n","  <div id=\"df-1a13c4e0-8a61-4e49-b2d0-60318b40db2b\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style 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<th>day</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>98001</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","      <td>0.611521</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2018</td>\n","      <td>6</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>98052</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>...</td>\n","      <td>2</td>\n","      <td>2.0</td>\n","      <td>1.077168</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2018</td>\n","      <td>6</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>98042</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","      <td>1.459666</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2018</td>\n","      <td>6</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>98028</td>\n","      <td>2</td>\n","      <td>NaN</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>2</td>\n","      <td>2.0</td>\n","      <td>0.602725</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2018</td>\n","      <td>6</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>98003</td>\n","      <td>2</td>\n","      <td>NaN</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","      <td>0.498099</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2018</td>\n","      <td>6</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>3838</th>\n","      <td>98122</td>\n","      <td>2</td>\n","      <td>NaN</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>0.799188</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>2.0</td>\n","      <td>2.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>30</td>\n","    </tr>\n","    <tr>\n","      <th>3839</th>\n","      <td>98033</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","      <td>1.024891</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>3.0</td>\n","      <td>3.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>30</td>\n","    </tr>\n","    <tr>\n","      <th>3840</th>\n","      <td>98108</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>0.459909</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>30</td>\n","    </tr>\n","    <tr>\n","      <th>3841</th>\n","      <td>98058</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>0.664518</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>30</td>\n","    </tr>\n","    <tr>\n","      <th>3842</th>\n","      <td>98006</td>\n","      <td>2</td>\n","      <td>NaN</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","      <td>2.430932</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>30</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3843 rows × 72 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a13c4e0-8a61-4e49-b2d0-60318b40db2b')\"\n","   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For example, if you wanted to compare education level to their perception of driving laws, you would call\n","#\"GetPerception(\"Education\")\" and be given a dataframe with two columns, highest level of education and their percetage of correct perception\n","#Of Washington driving laws.\n","\n","\n","\n","  finallist = [] #Initializes list where I will store dictionaries before I turn into Dataframe\n","  for index,row in df.iterrows():\n","  #I first initialize two dictionaries and a list that will be reset every row of the dataframe and appended to finallist above\n","    DictOfAverages = {} #Initializes Dictionary that will contain the ColumnName : ColumnNameValue and Perception : Average perception score\n","    ColumnNameValue = row[ColumnName]\n","    Perception = {}\n","    ListToAverage = []\n","    for item in FactChecker:\n","      Perception[item] = row[item]\n","      if Perception[item] == 3 or pd.isna(Perception[item]): #This removes values where the participant answered \"Not sure\" or didn't answer the question at all\n","        del Perception[item]\n","  #This for loop checks if their perception about a law is true or not and if it is, it counts as a 1, if not it counts as a zero\n","    for item in Perception:\n","      if Perception[item] == FactChecker[item]:\n","        ListToAverage.append(1)\n","      else:\n","        ListToAverage.append(0)\n","    if len(ListToAverage) != 0: #makes sure that there is at least one value in the list\n","      if pd.isna(ColumnNameValue) == False: #makes sure that they answered the question of education level\n","        Average = sum(ListToAverage)/len(ListToAverage)\n","        DictOfAverages[ColumnName] = ColumnNameValue\n","        DictOfAverages[\"Perception\"] = Average\n","        DictOfAverages[\"year\"] = row[\"year\"]\n","    finallist.append(DictOfAverages)\n","  FinalDataFrame = pd.DataFrame(finallist).dropna() #converts the list of Dictionaries into a pandas dataframe, dropping the NaN columns (if the participant didn't answer either of the questions)\n","  return FinalDataFrame\n","\n","GetPerception(\"Education\")\n","  "],"metadata":{"id":"hFP3oSESSUuT","executionInfo":{"status":"ok","timestamp":1677869291540,"user_tz":300,"elapsed":1098,"user":{"displayName":"Alexander Grande","userId":"11675376131495635883"}},"colab":{"base_uri":"https://localhost:8080/","height":423},"outputId":"0b37399a-272e-47d2-8dbd-82ee1a3faed5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Education  Perception    year\n","0           2.0    1.000000  2018.0\n","1           3.0    0.909091  2018.0\n","2           1.0    0.818182  2018.0\n","3           1.0    0.833333  2018.0\n","4           3.0    0.818182  2018.0\n","...         ...         ...     ...\n","3836        2.0    0.428571  2022.0\n","3838        2.0    0.692308  2022.0\n","3840        3.0    0.555556  2022.0\n","3841        2.0    0.769231  2022.0\n","3842        3.0    0.461538  2022.0\n","\n","[3780 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-c3a12a6a-5b3d-4d56-8062-64b5129572f8\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Education</th>\n","      <th>Perception</th>\n","      <th>year</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2.0</td>\n","      <td>1.000000</td>\n","      <td>2018.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>3.0</td>\n","      <td>0.909091</td>\n","      <td>2018.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>0.818182</td>\n","      <td>2018.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>0.833333</td>\n","      <td>2018.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3.0</td>\n","      <td>0.818182</td>\n","      <td>2018.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>3836</th>\n","      <td>2.0</td>\n","      <td>0.428571</td>\n","      <td>2022.0</td>\n","    </tr>\n","    <tr>\n","      <th>3838</th>\n","      <td>2.0</td>\n","      <td>0.692308</td>\n","      <td>2022.0</td>\n","    </tr>\n","    <tr>\n","      <th>3840</th>\n","      <td>3.0</td>\n","      <td>0.555556</td>\n","      <td>2022.0</td>\n","    </tr>\n","    <tr>\n","      <th>3841</th>\n","      <td>2.0</td>\n","      <td>0.769231</td>\n","      <td>2022.0</td>\n","    </tr>\n","    <tr>\n","      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Therefore it will be extremely easy to graph using plotly express\n","  y2018 = pd.DataFrame(dataframe[dataframe[\"year\"] == 2018.0].groupby(ColumnName)[\"Perception\"].mean()).reset_index()\n","  y2018['year'] = \"2018\"\n","  y2019 = pd.DataFrame(dataframe[dataframe[\"year\"] == 2019.0].groupby(ColumnName)[\"Perception\"].mean()).reset_index()\n","  y2019['year'] = \"2019\"\n","  y2021 = pd.DataFrame(dataframe[dataframe[\"year\"] == 2021.0].groupby(ColumnName)[\"Perception\"].mean()).reset_index()\n","  y2021['year'] = \"2021\"\n","  y2022 = pd.DataFrame(dataframe[dataframe[\"year\"] == 2022.0].groupby(ColumnName)[\"Perception\"].mean()).reset_index()\n","  y2022['year'] = \"2022\"\n","  combined = pd.concat([y2018, y2019, y2021, y2022])\n","  return combined\n","education = GetPerception(\"Education\")\n","education = GetYearMeans(education, \"Education\")\n","education"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"id":"yv5n0UUN75Xg","executionInfo":{"status":"ok","timestamp":1677871902567,"user_tz":300,"elapsed":2333,"user":{"displayName":"Alexander Grande","userId":"11675376131495635883"}},"outputId":"f8cd0566-87cc-43d6-c47f-6bb0c1f11551"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Education  Perception  year\n","0        1.0    0.819890  2018\n","1        2.0    0.833862  2018\n","2        3.0    0.849727  2018\n","3        4.0    0.829627  2018\n","0        1.0    0.826065  2019\n","1        2.0    0.829352  2019\n","2        3.0    0.839709  2019\n","3        4.0    0.802231  2019\n","0        1.0    0.766075  2021\n","1        2.0    0.791334  2021\n","2        3.0    0.799871  2021\n","3        4.0    0.704581  2021\n","0        1.0    0.773154  2022\n","1        2.0    0.837531  2022\n","2        3.0    0.795661  2022\n","3        4.0    0.719425  2022"],"text/html":["\n","  <div id=\"df-8938b4eb-b947-44cc-98be-353acbd7b1e2\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Education</th>\n","      <th>Perception</th>\n","      <th>year</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>0.819890</td>\n","      <td>2018</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>0.833862</td>\n","      <td>2018</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>0.849727</td>\n","      <td>2018</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>0.829627</td>\n","      <td>2018</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>0.826065</td>\n","      <td>2019</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>0.829352</td>\n","      <td>2019</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>0.839709</td>\n","      <td>2019</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>0.802231</td>\n","      <td>2019</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>0.766075</td>\n","      <td>2021</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>0.791334</td>\n","      <td>2021</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>0.799871</td>\n","      <td>2021</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>0.704581</td>\n","      <td>2021</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>0.773154</td>\n","      <td>2022</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>0.837531</td>\n","      <td>2022</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>0.795661</td>\n","      <td>2022</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>0.719425</td>\n","      <td>2022</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8938b4eb-b947-44cc-98be-353acbd7b1e2')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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inplace=True)\n","combined.replace(4, \"Master's Or Higher\", inplace=True)\n","fig = px.bar(combined, x=\"Education\", y=\"Perception\", color=\"year\", barmode='group')\n","fig.show()\n"],"metadata":{"id":"JtUkMVjbaikj","executionInfo":{"status":"ok","timestamp":1677872184778,"user_tz":300,"elapsed":1247,"user":{"displayName":"Alexander Grande","userId":"11675376131495635883"}},"colab":{"base_uri":"https://localhost:8080/","height":542},"outputId":"09303a16-386e-4500-aead-19829469e123"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 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