Pandas-read-write-files

README.md

@@ -9,6 +9,7 @@
   - [Installing](#installing)
 - [Usage](#usage)
 - [Contributing](#contributing)
+- [Resources](#resources)
 
 ## Overview
 
@@ -58,3 +59,7 @@ Add notes about how to use the system.
 
 See [CONTRIBUTING.md](CONTRIBUTING.md) for details.
 
+## Resources
+
+- https://realpython.com/python-virtual-environments-a-primer/
+- https://pandas.pydata.org/pandas-docs/stable/reference/frame.html

pandas-reading-writing-files/README.md

@@ -0,0 +1,18 @@
+# Reading and Writing Files With Pandas
+
+Pandas is a powerful and flexible Python package that allows you to work with
+labeled and time series data. It also provides statistics methods, enables
+plotting, and more. One crucial feature of Pandas is its ability to write and
+read Excel, CSV, and many other types of files. Functions like the Pandas
+read_csv() method enable you to work with files effectively. You can use them to
+save the data and labels from Pandas objects to a file and load them later as
+Pandas Series or DataFrame instances.
+
+
+## Topics
+
+- What the Pandas IO tools API is
+- How to read and write data to and from files
+- How to use the methods of read_csv()
+- How to work with various file formats
+- How to work with big data efficiently

pandas-reading-writing-files/data-columns.json

@@ -0,0 +1 @@
+{"COUNTRY":{"CHN":"China","IND":"India","USA":"US","IDN":"Indonesia","BRA":"Brazil","PAK":"Pakistan","NGA":"Nigeria","BGD":"Bangladesh","RUS":"Russia","MEX":"Mexico","JPN":"Japan","DEU":"Germany","FRA":"France","GBR":"UK","ITA":"Italy","ARG":"Argentina","DZA":"Algeria","CAN":"Canada","AUS":"Australia","KAZ":"Kazakhstan"},"POP":{"CHN":1398.72,"IND":1351.16,"USA":329.74,"IDN":268.07,"BRA":210.32,"PAK":205.71,"NGA":200.96,"BGD":167.09,"RUS":146.79,"MEX":126.58,"JPN":126.22,"DEU":83.02,"FRA":67.02,"GBR":66.44,"ITA":60.36,"ARG":44.94,"DZA":43.38,"CAN":37.59,"AUS":25.47,"KAZ":18.53},"AREA":{"CHN":9596.96,"IND":3287.26,"USA":9833.52,"IDN":1910.93,"BRA":8515.77,"PAK":881.91,"NGA":923.77,"BGD":147.57,"RUS":17098.25,"MEX":1964.38,"JPN":377.97,"DEU":357.11,"FRA":640.68,"GBR":242.5,"ITA":301.34,"ARG":2780.4,"DZA":2381.74,"CAN":9984.67,"AUS":7692.02,"KAZ":2724.9},"GDP":{"CHN":12234.78,"IND":2575.67,"USA":19485.39,"IDN":1015.54,"BRA":2055.51,"PAK":302.14,"NGA":375.77,"BGD":245.63,"RUS":1530.75,"MEX":1158.23,"JPN":4872.42,"DEU":3693.2,"FRA":2582.49,"GBR":2631.23,"ITA":1943.84,"ARG":637.49,"DZA":167.56,"CAN":1647.12,"AUS":1408.68,"KAZ":159.41},"CONT":{"CHN":"Asia","IND":"Asia","USA":"N.America","IDN":"Asia","BRA":"S.America","PAK":"Asia","NGA":"Africa","BGD":"Asia","RUS":null,"MEX":"N.America","JPN":"Asia","DEU":"Europe","FRA":"Europe","GBR":"Europe","ITA":"Europe","ARG":"S.America","DZA":"Africa","CAN":"N.America","AUS":"Oceania","KAZ":"Asia"},"IND_DAY":{"CHN":null,"IND":"1947-08-15","USA":"1776-07-04","IDN":"1945-08-17","BRA":"1822-09-07","PAK":"1947-08-14","NGA":"1960-10-01","BGD":"1971-03-26","RUS":"1992-06-12","MEX":"1810-09-16","JPN":null,"DEU":null,"FRA":"1789-07-14","GBR":null,"ITA":null,"ARG":"1816-07-09","DZA":"1962-07-05","CAN":"1867-07-01","AUS":null,"KAZ":"1991-12-16"}}

pandas-reading-writing-files/data-index.json

@@ -0,0 +1 @@
+{"CHN":{"COUNTRY":"China","POP":1398.72,"AREA":9596.96,"GDP":12234.78,"CONT":"Asia","IND_DAY":null},"IND":{"COUNTRY":"India","POP":1351.16,"AREA":3287.26,"GDP":2575.67,"CONT":"Asia","IND_DAY":"1947-08-15"},"USA":{"COUNTRY":"US","POP":329.74,"AREA":9833.52,"GDP":19485.39,"CONT":"N.America","IND_DAY":"1776-07-04"},"IDN":{"COUNTRY":"Indonesia","POP":268.07,"AREA":1910.93,"GDP":1015.54,"CONT":"Asia","IND_DAY":"1945-08-17"},"BRA":{"COUNTRY":"Brazil","POP":210.32,"AREA":8515.77,"GDP":2055.51,"CONT":"S.America","IND_DAY":"1822-09-07"},"PAK":{"COUNTRY":"Pakistan","POP":205.71,"AREA":881.91,"GDP":302.14,"CONT":"Asia","IND_DAY":"1947-08-14"},"NGA":{"COUNTRY":"Nigeria","POP":200.96,"AREA":923.77,"GDP":375.77,"CONT":"Africa","IND_DAY":"1960-10-01"},"BGD":{"COUNTRY":"Bangladesh","POP":167.09,"AREA":147.57,"GDP":245.63,"CONT":"Asia","IND_DAY":"1971-03-26"},"RUS":{"COUNTRY":"Russia","POP":146.79,"AREA":17098.25,"GDP":1530.75,"CONT":null,"IND_DAY":"1992-06-12"},"MEX":{"COUNTRY":"Mexico","POP":126.58,"AREA":1964.38,"GDP":1158.23,"CONT":"N.America","IND_DAY":"1810-09-16"},"JPN":{"COUNTRY":"Japan","POP":126.22,"AREA":377.97,"GDP":4872.42,"CONT":"Asia","IND_DAY":null},"DEU":{"COUNTRY":"Germany","POP":83.02,"AREA":357.11,"GDP":3693.2,"CONT":"Europe","IND_DAY":null},"FRA":{"COUNTRY":"France","POP":67.02,"AREA":640.68,"GDP":2582.49,"CONT":"Europe","IND_DAY":"1789-07-14"},"GBR":{"COUNTRY":"UK","POP":66.44,"AREA":242.5,"GDP":2631.23,"CONT":"Europe","IND_DAY":null},"ITA":{"COUNTRY":"Italy","POP":60.36,"AREA":301.34,"GDP":1943.84,"CONT":"Europe","IND_DAY":null},"ARG":{"COUNTRY":"Argentina","POP":44.94,"AREA":2780.4,"GDP":637.49,"CONT":"S.America","IND_DAY":"1816-07-09"},"DZA":{"COUNTRY":"Algeria","POP":43.38,"AREA":2381.74,"GDP":167.56,"CONT":"Africa","IND_DAY":"1962-07-05"},"CAN":{"COUNTRY":"Canada","POP":37.59,"AREA":9984.67,"GDP":1647.12,"CONT":"N.America","IND_DAY":"1867-07-01"},"AUS":{"COUNTRY":"Australia","POP":25.47,"AREA":7692.02,"GDP":1408.68,"CONT":"Oceania","IND_DAY":null},"KAZ":{"COUNTRY":"Kazakhstan","POP":18.53,"AREA":2724.9,"GDP":159.41,"CONT":"Asia","IND_DAY":"1991-12-16"}}

pandas-reading-writing-files/data-records.json

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+[{"COUNTRY":"China","POP":1398.72,"AREA":9596.96,"GDP":12234.78,"CONT":"Asia","IND_DAY":null},{"COUNTRY":"India","POP":1351.16,"AREA":3287.26,"GDP":2575.67,"CONT":"Asia","IND_DAY":"1947-08-15"},{"COUNTRY":"US","POP":329.74,"AREA":9833.52,"GDP":19485.39,"CONT":"N.America","IND_DAY":"1776-07-04"},{"COUNTRY":"Indonesia","POP":268.07,"AREA":1910.93,"GDP":1015.54,"CONT":"Asia","IND_DAY":"1945-08-17"},{"COUNTRY":"Brazil","POP":210.32,"AREA":8515.77,"GDP":2055.51,"CONT":"S.America","IND_DAY":"1822-09-07"},{"COUNTRY":"Pakistan","POP":205.71,"AREA":881.91,"GDP":302.14,"CONT":"Asia","IND_DAY":"1947-08-14"},{"COUNTRY":"Nigeria","POP":200.96,"AREA":923.77,"GDP":375.77,"CONT":"Africa","IND_DAY":"1960-10-01"},{"COUNTRY":"Bangladesh","POP":167.09,"AREA":147.57,"GDP":245.63,"CONT":"Asia","IND_DAY":"1971-03-26"},{"COUNTRY":"Russia","POP":146.79,"AREA":17098.25,"GDP":1530.75,"CONT":null,"IND_DAY":"1992-06-12"},{"COUNTRY":"Mexico","POP":126.58,"AREA":1964.38,"GDP":1158.23,"CONT":"N.America","IND_DAY":"1810-09-16"},{"COUNTRY":"Japan","POP":126.22,"AREA":377.97,"GDP":4872.42,"CONT":"Asia","IND_DAY":null},{"COUNTRY":"Germany","POP":83.02,"AREA":357.11,"GDP":3693.2,"CONT":"Europe","IND_DAY":null},{"COUNTRY":"France","POP":67.02,"AREA":640.68,"GDP":2582.49,"CONT":"Europe","IND_DAY":"1789-07-14"},{"COUNTRY":"UK","POP":66.44,"AREA":242.5,"GDP":2631.23,"CONT":"Europe","IND_DAY":null},{"COUNTRY":"Italy","POP":60.36,"AREA":301.34,"GDP":1943.84,"CONT":"Europe","IND_DAY":null},{"COUNTRY":"Argentina","POP":44.94,"AREA":2780.4,"GDP":637.49,"CONT":"S.America","IND_DAY":"1816-07-09"},{"COUNTRY":"Algeria","POP":43.38,"AREA":2381.74,"GDP":167.56,"CONT":"Africa","IND_DAY":"1962-07-05"},{"COUNTRY":"Canada","POP":37.59,"AREA":9984.67,"GDP":1647.12,"CONT":"N.America","IND_DAY":"1867-07-01"},{"COUNTRY":"Australia","POP":25.47,"AREA":7692.02,"GDP":1408.68,"CONT":"Oceania","IND_DAY":null},{"COUNTRY":"Kazakhstan","POP":18.53,"AREA":2724.9,"GDP":159.41,"CONT":"Asia","IND_DAY":"1991-12-16"}]

pandas-reading-writing-files/data-split.json

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+{"columns":["COUNTRY","POP","AREA","GDP","CONT","IND_DAY"],"index":["CHN","IND","USA","IDN","BRA","PAK","NGA","BGD","RUS","MEX","JPN","DEU","FRA","GBR","ITA","ARG","DZA","CAN","AUS","KAZ"],"data":[["China",1398.72,9596.96,12234.78,"Asia",null],["India",1351.16,3287.26,2575.67,"Asia","1947-08-15"],["US",329.74,9833.52,19485.39,"N.America","1776-07-04"],["Indonesia",268.07,1910.93,1015.54,"Asia","1945-08-17"],["Brazil",210.32,8515.77,2055.51,"S.America","1822-09-07"],["Pakistan",205.71,881.91,302.14,"Asia","1947-08-14"],["Nigeria",200.96,923.77,375.77,"Africa","1960-10-01"],["Bangladesh",167.09,147.57,245.63,"Asia","1971-03-26"],["Russia",146.79,17098.25,1530.75,null,"1992-06-12"],["Mexico",126.58,1964.38,1158.23,"N.America","1810-09-16"],["Japan",126.22,377.97,4872.42,"Asia",null],["Germany",83.02,357.11,3693.2,"Europe",null],["France",67.02,640.68,2582.49,"Europe","1789-07-14"],["UK",66.44,242.5,2631.23,"Europe",null],["Italy",60.36,301.34,1943.84,"Europe",null],["Argentina",44.94,2780.4,637.49,"S.America","1816-07-09"],["Algeria",43.38,2381.74,167.56,"Africa","1962-07-05"],["Canada",37.59,9984.67,1647.12,"N.America","1867-07-01"],["Australia",25.47,7692.02,1408.68,"Oceania",null],["Kazakhstan",18.53,2724.9,159.41,"Asia","1991-12-16"]]}

pandas-reading-writing-files/data-time.json

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+{"COUNTRY":{"CHN":"China","IND":"India","USA":"US","IDN":"Indonesia","BRA":"Brazil","PAK":"Pakistan","NGA":"Nigeria","BGD":"Bangladesh","RUS":"Russia","MEX":"Mexico","JPN":"Japan","DEU":"Germany","FRA":"France","GBR":"UK","ITA":"Italy","ARG":"Argentina","DZA":"Algeria","CAN":"Canada","AUS":"Australia","KAZ":"Kazakhstan"},"POP":{"CHN":1398.72,"IND":1351.16,"USA":329.74,"IDN":268.07,"BRA":210.32,"PAK":205.71,"NGA":200.96,"BGD":167.09,"RUS":146.79,"MEX":126.58,"JPN":126.22,"DEU":83.02,"FRA":67.02,"GBR":66.44,"ITA":60.36,"ARG":44.94,"DZA":43.38,"CAN":37.59,"AUS":25.47,"KAZ":18.53},"AREA":{"CHN":9596.96,"IND":3287.26,"USA":9833.52,"IDN":1910.93,"BRA":8515.77,"PAK":881.91,"NGA":923.77,"BGD":147.57,"RUS":17098.25,"MEX":1964.38,"JPN":377.97,"DEU":357.11,"FRA":640.68,"GBR":242.5,"ITA":301.34,"ARG":2780.4,"DZA":2381.74,"CAN":9984.67,"AUS":7692.02,"KAZ":2724.9},"GDP":{"CHN":12234.78,"IND":2575.67,"USA":19485.39,"IDN":1015.54,"BRA":2055.51,"PAK":302.14,"NGA":375.77,"BGD":245.63,"RUS":1530.75,"MEX":1158.23,"JPN":4872.42,"DEU":3693.2,"FRA":2582.49,"GBR":2631.23,"ITA":1943.84,"ARG":637.49,"DZA":167.56,"CAN":1647.12,"AUS":1408.68,"KAZ":159.41},"CONT":{"CHN":"Asia","IND":"Asia","USA":"N.America","IDN":"Asia","BRA":"S.America","PAK":"Asia","NGA":"Africa","BGD":"Asia","RUS":null,"MEX":"N.America","JPN":"Asia","DEU":"Europe","FRA":"Europe","GBR":"Europe","ITA":"Europe","ARG":"S.America","DZA":"Africa","CAN":"N.America","AUS":"Oceania","KAZ":"Asia"},"IND_DAY":{"CHN":null,"IND":-706320000000,"USA":-6106060800000,"IDN":-769219200000,"BRA":-4648924800000,"PAK":-706406400000,"NGA":-291945600000,"BGD":38793600000,"RUS":708307200000,"MEX":-5026838400000,"JPN":null,"DEU":null,"FRA":-5694969600000,"GBR":null,"ITA":null,"ARG":-4843411200000,"DZA":-236476800000,"CAN":-3234729600000,"AUS":null,"KAZ":692841600000}}

pandas-reading-writing-files/data.csv

@@ -0,0 +1,21 @@
+,COUNTRY,POP,AREA,GDP,CONT,IND_DAY
+CHN,China,1398.72,9596.96,12234.78,Asia,
+IND,India,1351.16,3287.26,2575.67,Asia,1947-08-15
+USA,US,329.74,9833.52,19485.39,N.America,1776-07-04
+IDN,Indonesia,268.07,1910.93,1015.54,Asia,1945-08-17
+BRA,Brazil,210.32,8515.77,2055.51,S.America,1822-09-07
+PAK,Pakistan,205.71,881.91,302.14,Asia,1947-08-14
+NGA,Nigeria,200.96,923.77,375.77,Africa,1960-10-01
+BGD,Bangladesh,167.09,147.57,245.63,Asia,1971-03-26
+RUS,Russia,146.79,17098.25,1530.75,,1992-06-12
+MEX,Mexico,126.58,1964.38,1158.23,N.America,1810-09-16
+JPN,Japan,126.22,377.97,4872.42,Asia,
+DEU,Germany,83.02,357.11,3693.2,Europe,
+FRA,France,67.02,640.68,2582.49,Europe,1789-07-14
+GBR,UK,66.44,242.5,2631.23,Europe,
+ITA,Italy,60.36,301.34,1943.84,Europe,
+ARG,Argentina,44.94,2780.4,637.49,S.America,1816-07-09
+DZA,Algeria,43.38,2381.74,167.56,Africa,1962-07-05
+CAN,Canada,37.59,9984.67,1647.12,N.America,1867-07-01
+AUS,Australia,25.47,7692.02,1408.68,Oceania,
+KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16

pandas-reading-writing-files/data.html

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+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>COUNTRY</th>
+      <th>POP</th>
+      <th>AREA</th>
+      <th>GDP</th>
+      <th>CONT</th>
+      <th>IND_DAY</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>CHN</th>
+      <td>China</td>
+      <td>1398.72</td>
+      <td>9596.96</td>
+      <td>12234.78</td>
+      <td>Asia</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>IND</th>
+      <td>India</td>
+      <td>1351.16</td>
+      <td>3287.26</td>
+      <td>2575.67</td>
+      <td>Asia</td>
+      <td>1947-08-15</td>
+    </tr>
+    <tr>
+      <th>USA</th>
+      <td>US</td>
+      <td>329.74</td>
+      <td>9833.52</td>
+      <td>19485.39</td>
+      <td>N.America</td>
+      <td>1776-07-04</td>
+    </tr>
+    <tr>
+      <th>IDN</th>
+      <td>Indonesia</td>
+      <td>268.07</td>
+      <td>1910.93</td>
+      <td>1015.54</td>
+      <td>Asia</td>
+      <td>1945-08-17</td>
+    </tr>
+    <tr>
+      <th>BRA</th>
+      <td>Brazil</td>
+      <td>210.32</td>
+      <td>8515.77</td>
+      <td>2055.51</td>
+      <td>S.America</td>
+      <td>1822-09-07</td>
+    </tr>
+    <tr>
+      <th>PAK</th>
+      <td>Pakistan</td>
+      <td>205.71</td>
+      <td>881.91</td>
+      <td>302.14</td>
+      <td>Asia</td>
+      <td>1947-08-14</td>
+    </tr>
+    <tr>
+      <th>NGA</th>
+      <td>Nigeria</td>
+      <td>200.96</td>
+      <td>923.77</td>
+      <td>375.77</td>
+      <td>Africa</td>
+      <td>1960-10-01</td>
+    </tr>
+    <tr>
+      <th>BGD</th>
+      <td>Bangladesh</td>
+      <td>167.09</td>
+      <td>147.57</td>
+      <td>245.63</td>
+      <td>Asia</td>
+      <td>1971-03-26</td>
+    </tr>
+    <tr>
+      <th>RUS</th>
+      <td>Russia</td>
+      <td>146.79</td>
+      <td>17098.25</td>
+      <td>1530.75</td>
+      <td>NaN</td>
+      <td>1992-06-12</td>
+    </tr>
+    <tr>
+      <th>MEX</th>
+      <td>Mexico</td>
+      <td>126.58</td>
+      <td>1964.38</td>
+      <td>1158.23</td>
+      <td>N.America</td>
+      <td>1810-09-16</td>
+    </tr>
+    <tr>
+      <th>JPN</th>
+      <td>Japan</td>
+      <td>126.22</td>
+      <td>377.97</td>
+      <td>4872.42</td>
+      <td>Asia</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>DEU</th>
+      <td>Germany</td>
+      <td>83.02</td>
+      <td>357.11</td>
+      <td>3693.2</td>
+      <td>Europe</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>FRA</th>
+      <td>France</td>
+      <td>67.02</td>
+      <td>640.68</td>
+      <td>2582.49</td>
+      <td>Europe</td>
+      <td>1789-07-14</td>
+    </tr>
+    <tr>
+      <th>GBR</th>
+      <td>UK</td>
+      <td>66.44</td>
+      <td>242.5</td>
+      <td>2631.23</td>
+      <td>Europe</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>ITA</th>
+      <td>Italy</td>
+      <td>60.36</td>
+      <td>301.34</td>
+      <td>1943.84</td>
+      <td>Europe</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>ARG</th>
+      <td>Argentina</td>
+      <td>44.94</td>
+      <td>2780.4</td>
+      <td>637.49</td>
+      <td>S.America</td>
+      <td>1816-07-09</td>
+    </tr>
+    <tr>
+      <th>DZA</th>
+      <td>Algeria</td>
+      <td>43.38</td>
+      <td>2381.74</td>
+      <td>167.56</td>
+      <td>Africa</td>
+      <td>1962-07-05</td>
+    </tr>
+    <tr>
+      <th>CAN</th>
+      <td>Canada</td>
+      <td>37.59</td>
+      <td>9984.67</td>
+      <td>1647.12</td>
+      <td>N.America</td>
+      <td>1867-07-01</td>
+    </tr>
+    <tr>
+      <th>AUS</th>
+      <td>Australia</td>
+      <td>25.47</td>
+      <td>7692.02</td>
+      <td>1408.68</td>
+      <td>Oceania</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>KAZ</th>
+      <td>Kazakhstan</td>
+      <td>18.53</td>
+      <td>2724.9</td>
+      <td>159.41</td>
+      <td>Asia</td>
+      <td>1991-12-16</td>
+    </tr>
+  </tbody>
+</table>

pandas-reading-writing-files/data.py

@@ -0,0 +1,43 @@
+data = {
+    'CHN': {'COUNTRY': 'China', 'POP': 1_398.72, 'AREA': 9_596.96,
+            'GDP': 12_234.78, 'CONT': 'Asia'},
+    'IND': {'COUNTRY': 'India', 'POP': 1_351.16, 'AREA': 3_287.26,
+            'GDP': 2_575.67, 'CONT': 'Asia', 'IND_DAY': '1947-08-15'},
+    'USA': {'COUNTRY': 'US', 'POP': 329.74, 'AREA': 9_833.52,
+            'GDP': 19_485.39, 'CONT': 'N.America',
+            'IND_DAY': '1776-07-04'},
+    'IDN': {'COUNTRY': 'Indonesia', 'POP': 268.07, 'AREA': 1_910.93,
+            'GDP': 1_015.54, 'CONT': 'Asia', 'IND_DAY': '1945-08-17'},
+    'BRA': {'COUNTRY': 'Brazil', 'POP': 210.32, 'AREA': 8_515.77,
+            'GDP': 2_055.51, 'CONT': 'S.America', 'IND_DAY': '1822-09-07'},
+    'PAK': {'COUNTRY': 'Pakistan', 'POP': 205.71, 'AREA': 881.91,
+            'GDP': 302.14, 'CONT': 'Asia', 'IND_DAY': '1947-08-14'},
+    'NGA': {'COUNTRY': 'Nigeria', 'POP': 200.96, 'AREA': 923.77,
+            'GDP': 375.77, 'CONT': 'Africa', 'IND_DAY': '1960-10-01'},
+    'BGD': {'COUNTRY': 'Bangladesh', 'POP': 167.09, 'AREA': 147.57,
+            'GDP': 245.63, 'CONT': 'Asia', 'IND_DAY': '1971-03-26'},
+    'RUS': {'COUNTRY': 'Russia', 'POP': 146.79, 'AREA': 17_098.25,
+            'GDP': 1_530.75, 'IND_DAY': '1992-06-12'},
+    'MEX': {'COUNTRY': 'Mexico', 'POP': 126.58, 'AREA': 1_964.38,
+            'GDP': 1_158.23, 'CONT': 'N.America', 'IND_DAY': '1810-09-16'},
+    'JPN': {'COUNTRY': 'Japan', 'POP': 126.22, 'AREA': 377.97,
+            'GDP': 4_872.42, 'CONT': 'Asia'},
+    'DEU': {'COUNTRY': 'Germany', 'POP': 83.02, 'AREA': 357.11,
+            'GDP': 3_693.20, 'CONT': 'Europe'},
+    'FRA': {'COUNTRY': 'France', 'POP': 67.02, 'AREA': 640.68,
+            'GDP': 2_582.49, 'CONT': 'Europe', 'IND_DAY': '1789-07-14'},
+    'GBR': {'COUNTRY': 'UK', 'POP': 66.44, 'AREA': 242.50,
+            'GDP': 2_631.23, 'CONT': 'Europe'},
+    'ITA': {'COUNTRY': 'Italy', 'POP': 60.36, 'AREA': 301.34,
+            'GDP': 1_943.84, 'CONT': 'Europe'},
+    'ARG': {'COUNTRY': 'Argentina', 'POP': 44.94, 'AREA': 2_780.40,
+            'GDP': 637.49, 'CONT': 'S.America', 'IND_DAY': '1816-07-09'},
+    'DZA': {'COUNTRY': 'Algeria', 'POP': 43.38, 'AREA': 2_381.74,
+            'GDP': 167.56, 'CONT': 'Africa', 'IND_DAY': '1962-07-05'},
+    'CAN': {'COUNTRY': 'Canada', 'POP': 37.59, 'AREA': 9_984.67,
+            'GDP': 1_647.12, 'CONT': 'N.America', 'IND_DAY': '1867-07-01'},
+    'AUS': {'COUNTRY': 'Australia', 'POP': 25.47, 'AREA': 7_692.02,
+            'GDP': 1_408.68, 'CONT': 'Oceania'},
+    'KAZ': {'COUNTRY': 'Kazakhstan', 'POP': 18.53, 'AREA': 2_724.90,
+            'GDP': 159.41, 'CONT': 'Asia', 'IND_DAY': '1991-12-16'}
+}

pandas-reading-writing-files/formatted-data.csv

@@ -0,0 +1,21 @@
+,COUNTRY,POP,AREA,GDP,CONT,IND_DAY
+CHN,China,1398.72,9596.96,12234.78,Asia,
+IND,India,1351.16,3287.26,2575.67,Asia,August 15 1947
+USA,US,329.74,9833.52,19485.39,N.America,July 04 1776
+IDN,Indonesia,268.07,1910.93,1015.54,Asia,August 17 1945
+BRA,Brazil,210.32,8515.77,2055.51,S.America,September 07 1822
+PAK,Pakistan,205.71,881.91,302.14,Asia,August 14 1947
+NGA,Nigeria,200.96,923.77,375.77,Africa,October 01 1960
+BGD,Bangladesh,167.09,147.57,245.63,Asia,March 26 1971
+RUS,Russia,146.79,17098.25,1530.75,,June 12 1992
+MEX,Mexico,126.58,1964.38,1158.23,N.America,September 16 1810
+JPN,Japan,126.22,377.97,4872.42,Asia,
+DEU,Germany,83.02,357.11,3693.2,Europe,
+FRA,France,67.02,640.68,2582.49,Europe,July 14 1789
+GBR,UK,66.44,242.5,2631.23,Europe,
+ITA,Italy,60.36,301.34,1943.84,Europe,
+ARG,Argentina,44.94,2780.4,637.49,S.America,July 09 1816
+DZA,Algeria,43.38,2381.74,167.56,Africa,July 05 1962
+CAN,Canada,37.59,9984.67,1647.12,N.America,July 01 1867
+AUS,Australia,25.47,7692.02,1408.68,Oceania,
+KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,December 16 1991