india-gdp-growth-eda.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots","metadata":{"execution":{"iopub.status.busy":"2022-12-23T10:13:54.932701Z","iopub.execute_input":"2022-12-23T10:13:54.933119Z","iopub.status.idle":"2022-12-23T10:13:54.940784Z","shell.execute_reply.started":"2022-12-23T10:13:54.933087Z","shell.execute_reply":"2022-12-23T10:13:54.939404Z"},"trusted":true},"execution_count":127,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/indian-gdp/India_GDP_Data.csv')\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:44.837966Z","iopub.execute_input":"2022-12-23T09:43:44.838365Z","iopub.status.idle":"2022-12-23T09:43:44.856651Z","shell.execute_reply.started":"2022-12-23T09:43:44.838333Z","shell.execute_reply":"2022-12-23T09:43:44.855449Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"   Year  GDP_In_Billion_USD  Per_Capita_in_USD  Percentage_Growth \n0  2021             3173.40               2277                8.95\n1  2020             2667.69               1933               -6.60\n2  2019             2831.55               2072                3.74\n3  2018             2702.93               1998                6.45\n4  2017             2651.47               1981                6.80","text/html":"<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>Year</th>\n      <th>GDP_In_Billion_USD</th>\n      <th>Per_Capita_in_USD</th>\n      <th>Percentage_Growth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2021</td>\n      <td>3173.40</td>\n      <td>2277</td>\n      <td>8.95</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2020</td>\n      <td>2667.69</td>\n      <td>1933</td>\n      <td>-6.60</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2019</td>\n      <td>2831.55</td>\n      <td>2072</td>\n      <td>3.74</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018</td>\n      <td>2702.93</td>\n      <td>1998</td>\n      <td>6.45</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2017</td>\n      <td>2651.47</td>\n      <td>1981</td>\n      <td>6.80</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:44.908160Z","iopub.execute_input":"2022-12-23T09:43:44.909199Z","iopub.status.idle":"2022-12-23T09:43:44.918079Z","shell.execute_reply.started":"2022-12-23T09:43:44.909157Z","shell.execute_reply":"2022-12-23T09:43:44.916838Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"Year                  0\nGDP_In_Billion_USD    0\nPer_Capita_in_USD     0\nPercentage_Growth     0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:44.978300Z","iopub.execute_input":"2022-12-23T09:43:44.978716Z","iopub.status.idle":"2022-12-23T09:43:44.991860Z","shell.execute_reply.started":"2022-12-23T09:43:44.978682Z","shell.execute_reply":"2022-12-23T09:43:44.990663Z"},"trusted":true},"execution_count":91,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 61 entries, 0 to 60\nData columns (total 4 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   Year                61 non-null     int64  \n 1   GDP_In_Billion_USD  61 non-null     float64\n 2   Per_Capita_in_USD   61 non-null     int64  \n 3   Percentage_Growth   61 non-null     float64\ndtypes: float64(2), int64(2)\nmemory usage: 2.0 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"data.describe()","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:45.028175Z","iopub.execute_input":"2022-12-23T09:43:45.028613Z","iopub.status.idle":"2022-12-23T09:43:45.053094Z","shell.execute_reply.started":"2022-12-23T09:43:45.028577Z","shell.execute_reply":"2022-12-23T09:43:45.051910Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"              Year  GDP_In_Billion_USD  Per_Capita_in_USD  Percentage_Growth \ncount    61.000000           61.000000          61.000000           61.000000\nmean   1991.000000          709.889016         611.163934            5.089344\nstd      17.752934          870.169692         618.757227            3.282711\nmin    1961.000000           39.230000          85.000000           -6.600000\n25%    1976.000000          102.720000         163.000000            3.740000\n50%    1991.000000          296.040000         346.000000            5.710000\n75%    2006.000000          940.260000         807.000000            7.550000\nmax    2021.000000         3173.400000        2277.000000            9.630000","text/html":"<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>Year</th>\n      <th>GDP_In_Billion_USD</th>\n      <th>Per_Capita_in_USD</th>\n      <th>Percentage_Growth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>61.000000</td>\n      <td>61.000000</td>\n      <td>61.000000</td>\n      <td>61.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1991.000000</td>\n      <td>709.889016</td>\n      <td>611.163934</td>\n      <td>5.089344</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>17.752934</td>\n      <td>870.169692</td>\n      <td>618.757227</td>\n      <td>3.282711</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1961.000000</td>\n      <td>39.230000</td>\n      <td>85.000000</td>\n      <td>-6.600000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1976.000000</td>\n      <td>102.720000</td>\n      <td>163.000000</td>\n      <td>3.740000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1991.000000</td>\n      <td>296.040000</td>\n      <td>346.000000</td>\n      <td>5.710000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2006.000000</td>\n      <td>940.260000</td>\n      <td>807.000000</td>\n      <td>7.550000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2021.000000</td>\n      <td>3173.400000</td>\n      <td>2277.000000</td>\n      <td>9.630000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.columns = ['Year', 'GDP_Billion_USD', 'Per_Capita_USD',\n       'Percentage_Growth']","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:45.118793Z","iopub.execute_input":"2022-12-23T09:43:45.120100Z","iopub.status.idle":"2022-12-23T09:43:45.126007Z","shell.execute_reply.started":"2022-12-23T09:43:45.120047Z","shell.execute_reply":"2022-12-23T09:43:45.124731Z"},"trusted":true},"execution_count":93,"outputs":[]},{"cell_type":"code","source":"data.sort_values('Year', ascending=True, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:45.138214Z","iopub.execute_input":"2022-12-23T09:43:45.139060Z","iopub.status.idle":"2022-12-23T09:43:45.145975Z","shell.execute_reply.started":"2022-12-23T09:43:45.139022Z","shell.execute_reply":"2022-12-23T09:43:45.144961Z"},"trusted":true},"execution_count":94,"outputs":[]},{"cell_type":"code","source":"data['Pop_M'] = 1000 * data['GDP_Billion_USD']/ data['Per_Capita_USD']\ndata['Pop_Growth'] = data['Pop_M'].pct_change()\ndata['Pop_Growth'] *= 100\ndata['Per_Capita_Growth'] = data['Per_Capita_USD'].pct_change()\ndata['Per_Capita_Growth'] *= 100","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:45.183390Z","iopub.execute_input":"2022-12-23T09:43:45.183804Z","iopub.status.idle":"2022-12-23T09:43:45.194639Z","shell.execute_reply.started":"2022-12-23T09:43:45.183770Z","shell.execute_reply":"2022-12-23T09:43:45.193404Z"},"trusted":true},"execution_count":95,"outputs":[]},{"cell_type":"code","source":"data.dropna().reset_index(drop = True)","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:45.218062Z","iopub.execute_input":"2022-12-23T09:43:45.218502Z","iopub.status.idle":"2022-12-23T09:43:45.253089Z","shell.execute_reply.started":"2022-12-23T09:43:45.218451Z","shell.execute_reply":"2022-12-23T09:43:45.251945Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"    Year  GDP_Billion_USD  Per_Capita_USD  Percentage_Growth        Pop_M  \\\n0   1962            42.16              90               2.93   468.444444   \n1   1963            48.42             101               5.99   479.405941   \n2   1964            56.48             116               7.45   486.896552   \n3   1965            59.55             119              -2.64   500.420168   \n4   1966            45.87              90              -0.06   509.666667   \n5   1967            50.13              96               7.83   522.187500   \n6   1968            53.09             100               3.39   530.900000   \n7   1969            58.45             108               6.54   541.203704   \n8   1970            62.42             112               5.16   557.321429   \n9   1971            67.35             119               1.64   565.966387   \n10  1972            71.46             123              -0.55   580.975610   \n11  1973            85.52             144               3.30   593.888889   \n12  1974            99.53             163               1.19   610.613497   \n13  1975            98.47             158               9.15   623.227848   \n14  1976           102.72             161               1.66   638.012422   \n15  1977           121.49             186               7.25   653.172043   \n16  1978           137.30             206               5.71   666.504854   \n17  1979           152.99             224              -5.24   682.991071   \n18  1980           186.33             267               6.74   697.865169   \n19  1981           193.49             270               6.01   716.629630   \n20  1982           200.72             274               3.48   732.554745   \n21  1983           218.26             291               7.29   750.034364   \n22  1984           212.16             277               3.82   765.920578   \n23  1985           232.51             296               5.25   785.506757   \n24  1986           248.99             310               4.78   803.193548   \n25  1987           279.03             340               3.97   820.676471   \n26  1988           296.59             354               9.63   837.824859   \n27  1989           296.04             346               5.95   855.606936   \n28  1990           320.98             368               5.53   872.228261   \n29  1991           270.11             303               1.06   891.452145   \n30  1992           288.21             317               5.48   909.179811   \n31  1993           279.30             301               4.75   927.906977   \n32  1994           327.28             346               6.66   945.895954   \n33  1995           360.28             374               7.57   963.315508   \n34  1996           392.90             400               7.55   982.250000   \n35  1997           415.87             415               4.05  1002.096386   \n36  1998           421.35             413               6.18  1020.217918   \n37  1999           458.82             442               8.85  1038.054299   \n38  2000           468.39             443               3.84  1057.313770   \n39  2001           485.44             452               4.82  1073.982301   \n40  2002           514.94             471               3.80  1093.290870   \n41  2003           607.70             547               7.86  1110.968921   \n42  2004           709.15             628               7.92  1129.219745   \n43  2005           820.38             715               7.92  1147.384615   \n44  2006           940.26             807               8.06  1165.130112   \n45  2007          1216.74            1028               7.66  1183.599222   \n46  2008          1198.90             999               3.09  1200.100100   \n47  2009          1341.89            1102               7.86  1217.686025   \n48  2010          1675.62            1358               8.50  1233.888071   \n49  2011          1823.05            1458               5.24  1250.377229   \n50  2012          1827.64            1444               5.46  1265.678670   \n51  2013          1856.72            1450               6.39  1280.496552   \n52  2014          2039.13            1574               7.41  1295.508259   \n53  2015          2103.59            1606               8.00  1309.831880   \n54  2016          2294.80            1733               8.26  1324.177726   \n55  2017          2651.47            1981               6.80  1338.450278   \n56  2018          2702.93            1998               6.45  1352.817818   \n57  2019          2831.55            2072               3.74  1366.578185   \n58  2020          2667.69            1933              -6.60  1380.077600   \n59  2021          3173.40            2277               8.95  1393.675889   \n\n    Pop_Growth  Per_Capita_Growth  \n0     1.498286           5.882353  \n1     2.339978          12.222222  \n2     1.562478          14.851485  \n3     2.777513           2.586207  \n4     1.847747         -24.369748  \n5     2.456671           6.666667  \n6     1.668462           4.166667  \n7     1.940799           8.000000  \n8     2.978125           3.703704  \n9     1.551162           6.250000  \n10    2.651964           3.361345  \n11    2.222689          17.073171  \n12    2.816117          13.194444  \n13    2.065849          -3.067485  \n14    2.372258           1.898734  \n15    2.376070          15.527950  \n16    2.041240          10.752688  \n17    2.473533           8.737864  \n18    2.177788          19.196429  \n19    2.688838           1.123596  \n20    2.222224           1.481481  \n21    2.386118           6.204380  \n22    2.118065          -4.810997  \n23    2.557208           6.859206  \n24    2.251641           4.729730  \n25    2.176676           9.677419  \n26    2.089543           4.117647  \n27    2.122410          -2.259887  \n28    1.942636           6.358382  \n29    2.203997         -17.663043  \n30    1.988628           4.620462  \n31    2.059787          -5.047319  \n32    1.938662          14.950166  \n33    1.841593           8.092486  \n34    1.965555           6.951872  \n35    2.020502           3.750000  \n36    1.808362          -0.481928  \n37    1.748291           7.021792  \n38    1.855343           0.226244  \n39    1.576498           2.031603  \n40    1.797848           4.203540  \n41    1.616958          16.135881  \n42    1.642784          14.808044  \n43    1.608621          13.853503  \n44    1.546604          12.867133  \n45    1.585154          27.385378  \n46    1.394127          -2.821012  \n47    1.465372          10.310310  \n48    1.330560          23.230490  \n49    1.336358           7.363770  \n50    1.223746          -0.960219  \n51    1.170746           0.415512  \n52    1.172335           8.551724  \n53    1.105637           2.033037  \n54    1.095243           7.907846  \n55    1.077843          14.310444  \n56    1.073446           0.858152  \n57    1.017163           3.703704  \n58    0.987826          -6.708494  \n59    0.985328          17.796172  ","text/html":"<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>Year</th>\n      <th>GDP_Billion_USD</th>\n      <th>Per_Capita_USD</th>\n      <th>Percentage_Growth</th>\n      <th>Pop_M</th>\n      <th>Pop_Growth</th>\n      <th>Per_Capita_Growth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1962</td>\n      <td>42.16</td>\n      <td>90</td>\n      <td>2.93</td>\n      <td>468.444444</td>\n      <td>1.498286</td>\n      <td>5.882353</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1963</td>\n      <td>48.42</td>\n      <td>101</td>\n      <td>5.99</td>\n      <td>479.405941</td>\n      <td>2.339978</td>\n      <td>12.222222</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1964</td>\n      <td>56.48</td>\n      <td>116</td>\n      <td>7.45</td>\n      <td>486.896552</td>\n      <td>1.562478</td>\n      <td>14.851485</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1965</td>\n      <td>59.55</td>\n      <td>119</td>\n      <td>-2.64</td>\n      <td>500.420168</td>\n      <td>2.777513</td>\n      <td>2.586207</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1966</td>\n      <td>45.87</td>\n      <td>90</td>\n      <td>-0.06</td>\n      <td>509.666667</td>\n      <td>1.847747</td>\n      <td>-24.369748</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1967</td>\n      <td>50.13</td>\n      <td>96</td>\n      <td>7.83</td>\n      <td>522.187500</td>\n      <td>2.456671</td>\n      <td>6.666667</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1968</td>\n      <td>53.09</td>\n      <td>100</td>\n      <td>3.39</td>\n      <td>530.900000</td>\n      <td>1.668462</td>\n      <td>4.166667</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1969</td>\n      <td>58.45</td>\n      <td>108</td>\n      <td>6.54</td>\n      <td>541.203704</td>\n      <td>1.940799</td>\n      <td>8.000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1970</td>\n      <td>62.42</td>\n      <td>112</td>\n      <td>5.16</td>\n      <td>557.321429</td>\n      <td>2.978125</td>\n      <td>3.703704</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1971</td>\n      <td>67.35</td>\n      <td>119</td>\n      <td>1.64</td>\n      <td>565.966387</td>\n      <td>1.551162</td>\n      <td>6.250000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1972</td>\n      <td>71.46</td>\n      <td>123</td>\n      <td>-0.55</td>\n      <td>580.975610</td>\n      <td>2.651964</td>\n      <td>3.361345</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1973</td>\n      <td>85.52</td>\n      <td>144</td>\n      <td>3.30</td>\n      <td>593.888889</td>\n      <td>2.222689</td>\n      <td>17.073171</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1974</td>\n      <td>99.53</td>\n      <td>163</td>\n      <td>1.19</td>\n      <td>610.613497</td>\n      <td>2.816117</td>\n      <td>13.194444</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1975</td>\n      <td>98.47</td>\n      <td>158</td>\n      <td>9.15</td>\n      <td>623.227848</td>\n      <td>2.065849</td>\n      <td>-3.067485</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1976</td>\n      <td>102.72</td>\n      <td>161</td>\n      <td>1.66</td>\n      <td>638.012422</td>\n      <td>2.372258</td>\n      <td>1.898734</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>1977</td>\n      <td>121.49</td>\n      <td>186</td>\n      <td>7.25</td>\n      <td>653.172043</td>\n      <td>2.376070</td>\n      <td>15.527950</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>1978</td>\n      <td>137.30</td>\n      <td>206</td>\n      <td>5.71</td>\n      <td>666.504854</td>\n      <td>2.041240</td>\n      <td>10.752688</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>1979</td>\n      <td>152.99</td>\n      <td>224</td>\n      <td>-5.24</td>\n      <td>682.991071</td>\n      <td>2.473533</td>\n      <td>8.737864</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>1980</td>\n      <td>186.33</td>\n      <td>267</td>\n      <td>6.74</td>\n      <td>697.865169</td>\n      <td>2.177788</td>\n      <td>19.196429</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>1981</td>\n      <td>193.49</td>\n      <td>270</td>\n      <td>6.01</td>\n      <td>716.629630</td>\n      <td>2.688838</td>\n      <td>1.123596</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>1982</td>\n      <td>200.72</td>\n      <td>274</td>\n      <td>3.48</td>\n      <td>732.554745</td>\n      <td>2.222224</td>\n      <td>1.481481</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>1983</td>\n      <td>218.26</td>\n      <td>291</td>\n      <td>7.29</td>\n      <td>750.034364</td>\n      <td>2.386118</td>\n      <td>6.204380</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>1984</td>\n      <td>212.16</td>\n      <td>277</td>\n      <td>3.82</td>\n      <td>765.920578</td>\n      <td>2.118065</td>\n      <td>-4.810997</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>1985</td>\n      <td>232.51</td>\n      <td>296</td>\n      <td>5.25</td>\n      <td>785.506757</td>\n      <td>2.557208</td>\n      <td>6.859206</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>1986</td>\n      <td>248.99</td>\n      <td>310</td>\n      <td>4.78</td>\n      <td>803.193548</td>\n      <td>2.251641</td>\n      <td>4.729730</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>1987</td>\n      <td>279.03</td>\n      <td>340</td>\n      <td>3.97</td>\n      <td>820.676471</td>\n      <td>2.176676</td>\n      <td>9.677419</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>1988</td>\n      <td>296.59</td>\n      <td>354</td>\n      <td>9.63</td>\n      <td>837.824859</td>\n      <td>2.089543</td>\n      <td>4.117647</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>1989</td>\n      <td>296.04</td>\n      <td>346</td>\n      <td>5.95</td>\n      <td>855.606936</td>\n      <td>2.122410</td>\n      <td>-2.259887</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>1990</td>\n      <td>320.98</td>\n      <td>368</td>\n      <td>5.53</td>\n      <td>872.228261</td>\n      <td>1.942636</td>\n      <td>6.358382</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>1991</td>\n      <td>270.11</td>\n      <td>303</td>\n      <td>1.06</td>\n      <td>891.452145</td>\n      <td>2.203997</td>\n      <td>-17.663043</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>1992</td>\n      <td>288.21</td>\n      <td>317</td>\n      <td>5.48</td>\n      <td>909.179811</td>\n      <td>1.988628</td>\n      <td>4.620462</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>1993</td>\n      <td>279.30</td>\n      <td>301</td>\n      <td>4.75</td>\n      <td>927.906977</td>\n      <td>2.059787</td>\n      <td>-5.047319</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>1994</td>\n      <td>327.28</td>\n      <td>346</td>\n      <td>6.66</td>\n      <td>945.895954</td>\n      <td>1.938662</td>\n      <td>14.950166</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>1995</td>\n      <td>360.28</td>\n      <td>374</td>\n      <td>7.57</td>\n      <td>963.315508</td>\n      <td>1.841593</td>\n      <td>8.092486</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>1996</td>\n      <td>392.90</td>\n      <td>400</td>\n      <td>7.55</td>\n      <td>982.250000</td>\n      <td>1.965555</td>\n      <td>6.951872</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>1997</td>\n      <td>415.87</td>\n      <td>415</td>\n      <td>4.05</td>\n      <td>1002.096386</td>\n      <td>2.020502</td>\n      <td>3.750000</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>1998</td>\n      <td>421.35</td>\n      <td>413</td>\n      <td>6.18</td>\n      <td>1020.217918</td>\n      <td>1.808362</td>\n      <td>-0.481928</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>1999</td>\n      <td>458.82</td>\n      <td>442</td>\n      <td>8.85</td>\n      <td>1038.054299</td>\n      <td>1.748291</td>\n      <td>7.021792</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>2000</td>\n      <td>468.39</td>\n      <td>443</td>\n      <td>3.84</td>\n      <td>1057.313770</td>\n      <td>1.855343</td>\n      <td>0.226244</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>2001</td>\n      <td>485.44</td>\n      <td>452</td>\n      <td>4.82</td>\n      <td>1073.982301</td>\n      <td>1.576498</td>\n      <td>2.031603</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>2002</td>\n      <td>514.94</td>\n      <td>471</td>\n      <td>3.80</td>\n      <td>1093.290870</td>\n      <td>1.797848</td>\n      <td>4.203540</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>2003</td>\n      <td>607.70</td>\n      <td>547</td>\n      <td>7.86</td>\n      <td>1110.968921</td>\n      <td>1.616958</td>\n      <td>16.135881</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>2004</td>\n      <td>709.15</td>\n      <td>628</td>\n      <td>7.92</td>\n      <td>1129.219745</td>\n      <td>1.642784</td>\n      <td>14.808044</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>2005</td>\n      <td>820.38</td>\n      <td>715</td>\n      <td>7.92</td>\n      <td>1147.384615</td>\n      <td>1.608621</td>\n      <td>13.853503</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>2006</td>\n      <td>940.26</td>\n      <td>807</td>\n      <td>8.06</td>\n      <td>1165.130112</td>\n      <td>1.546604</td>\n      <td>12.867133</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>2007</td>\n      <td>1216.74</td>\n      <td>1028</td>\n      <td>7.66</td>\n      <td>1183.599222</td>\n      <td>1.585154</td>\n      <td>27.385378</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>2008</td>\n      <td>1198.90</td>\n      <td>999</td>\n      <td>3.09</td>\n      <td>1200.100100</td>\n      <td>1.394127</td>\n      <td>-2.821012</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>2009</td>\n      <td>1341.89</td>\n      <td>1102</td>\n      <td>7.86</td>\n      <td>1217.686025</td>\n      <td>1.465372</td>\n      <td>10.310310</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>2010</td>\n      <td>1675.62</td>\n      <td>1358</td>\n      <td>8.50</td>\n      <td>1233.888071</td>\n      <td>1.330560</td>\n      <td>23.230490</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>2011</td>\n      <td>1823.05</td>\n      <td>1458</td>\n      <td>5.24</td>\n      <td>1250.377229</td>\n      <td>1.336358</td>\n      <td>7.363770</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>2012</td>\n      <td>1827.64</td>\n      <td>1444</td>\n      <td>5.46</td>\n      <td>1265.678670</td>\n      <td>1.223746</td>\n      <td>-0.960219</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>2013</td>\n      <td>1856.72</td>\n      <td>1450</td>\n      <td>6.39</td>\n      <td>1280.496552</td>\n      <td>1.170746</td>\n      <td>0.415512</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>2014</td>\n      <td>2039.13</td>\n      <td>1574</td>\n      <td>7.41</td>\n      <td>1295.508259</td>\n      <td>1.172335</td>\n      <td>8.551724</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>2015</td>\n      <td>2103.59</td>\n      <td>1606</td>\n      <td>8.00</td>\n      <td>1309.831880</td>\n      <td>1.105637</td>\n      <td>2.033037</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>2016</td>\n      <td>2294.80</td>\n      <td>1733</td>\n      <td>8.26</td>\n      <td>1324.177726</td>\n      <td>1.095243</td>\n      <td>7.907846</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>2017</td>\n      <td>2651.47</td>\n      <td>1981</td>\n      <td>6.80</td>\n      <td>1338.450278</td>\n      <td>1.077843</td>\n      <td>14.310444</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>2018</td>\n      <td>2702.93</td>\n      <td>1998</td>\n      <td>6.45</td>\n      <td>1352.817818</td>\n      <td>1.073446</td>\n      <td>0.858152</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>2019</td>\n      <td>2831.55</td>\n      <td>2072</td>\n      <td>3.74</td>\n      <td>1366.578185</td>\n      <td>1.017163</td>\n      <td>3.703704</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>2020</td>\n      <td>2667.69</td>\n      <td>1933</td>\n      <td>-6.60</td>\n      <td>1380.077600</td>\n      <td>0.987826</td>\n      <td>-6.708494</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>2021</td>\n      <td>3173.40</td>\n      <td>2277</td>\n      <td>8.95</td>\n      <td>1393.675889</td>\n      <td>0.985328</td>\n      <td>17.796172</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# EDA","metadata":{}},{"cell_type":"markdown","source":"### Barplot showing growth of india's GDP and Per Capita Income per year from 1961 to 2021","metadata":{}},{"cell_type":"code","source":"fig = px.bar(data, x = 'Year', y = 'GDP_Billion_USD', color = 'Per_Capita_USD')\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-12-23T09:43:45.898814Z","iopub.execute_input":"2022-12-23T09:43:45.899378Z","iopub.status.idle":"2022-12-23T09:43:45.994064Z","shell.execute_reply.started":"2022-12-23T09:43:45.899327Z","shell.execute_reply":"2022-12-23T09:43:45.992778Z"},"trusted":true},"execution_count":97,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"6c618a1f-aa56-4b99-b026-30ca1e6eecff\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6c618a1f-aa56-4b99-b026-30ca1e6eecff\")) {                    Plotly.newPlot(                        \"6c618a1f-aa56-4b99-b026-30ca1e6eecff\",                        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               NaN  \n59    1.498286           5.882353  \n58    2.339978          12.222222  \n57    1.562478          14.851485  \n56    2.777513           2.586207  ","text/html":"<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>Year</th>\n      <th>GDP_Billion_USD</th>\n      <th>Per_Capita_USD</th>\n      <th>Percentage_Growth</th>\n      <th>Pop_M</th>\n      <th>Pop_Growth</th>\n      <th>Per_Capita_Growth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>60</th>\n      <td>1961</td>\n      <td>39.23</td>\n      <td>85</td>\n      <td>3.72</td>\n      <td>461.529412</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>1962</td>\n      <td>42.16</td>\n      <td>90</td>\n      <td>2.93</td>\n      <td>468.444444</td>\n      <td>1.498286</td>\n      <td>5.882353</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>1963</td>\n      <td>48.42</td>\n      <td>101</td>\n      <td>5.99</td>\n      <td>479.405941</td>\n      <td>2.339978</td>\n      <td>12.222222</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>1964</td>\n      <td>56.48</td>\n      <td>116</td>\n      <td>7.45</td>\n      <td>486.896552</td>\n      <td>1.562478</td>\n      <td>14.851485</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>1965</td>\n      <td>59.55</td>\n      <td>119</td>\n      <td>-2.64</td>\n      <td>500.420168</td>\n      <td>2.777513</td>\n      <td>2.586207</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"fig = px.bar(data, x = 'Year', y = 'Per_Capita_USD', color = 'Per_Capita_Growth', title = 'Per Capita Income vs Per Capita Income 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width=800\n)\n\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-12-23T10:27:47.973699Z","iopub.execute_input":"2022-12-23T10:27:47.974082Z","iopub.status.idle":"2022-12-23T10:27:48.039769Z","shell.execute_reply.started":"2022-12-23T10:27:47.974052Z","shell.execute_reply":"2022-12-23T10:27:48.038535Z"},"trusted":true},"execution_count":139,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"bc3b1295-ef8e-4ecd-8fb8-bae765482d64\" class=\"plotly-graph-div\" style=\"height:800px; width:800px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"bc3b1295-ef8e-4ecd-8fb8-bae765482d64\")) {                    Plotly.newPlot(                        \"bc3b1295-ef8e-4ecd-8fb8-bae765482d64\",                        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