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‎contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md

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# Introduction_to_Pandas_Library_and_DataFrames
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> Content Creator - Krishna Kaushik
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**As you have learnt Python Programming , now it's time for some applications.**
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- Machine Learning and Data Science is the emerging field of today's time , to work in this this field your first step should be `Data Science` as Machine Learning is all about data.
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# Let's create
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cars_with_colours = pd.DataFrame({"Cars" : ["BMW","Audi","Thar","Honda"],
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"Colour" : ["Black","White","Red","Green"]})
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cars_with_colours
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print(cars_with_colours)
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```
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>Cars</th>
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<th>Colour</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>BMW</td>
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<td>Black</td>
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</tr>
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<tr>
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<th>1</th>
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<td>Audi</td>
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<td>White</td>
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</tr>
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<tr>
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<th>2</th>
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<td>Thar</td>
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<td>Red</td>
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</tr>
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<tr>
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<th>3</th>
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<td>Honda</td>
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<td>Green</td>
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</tr>
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</tbody>
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</table>
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</div>
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Cars Colour
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0 BMW Black
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1 Audi White
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2 Thar Red
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3 Honda Green
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The dictionary key is the `column name` and value are the `column data`.
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record = pd.DataFrame({"Student_Name":students ,
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"Age" :age})
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record
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print(record)
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```
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>Student_Name</th>
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<th>Age</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>Ram</td>
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<td>19</td>
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</tr>
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<tr>
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<th>1</th>
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<td>Mohan</td>
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<td>20</td>
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</tr>
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<tr>
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<th>2</th>
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<td>Krishna</td>
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<td>21</td>
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</tr>
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<tr>
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<th>3</th>
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<td>Shivam</td>
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<td>24</td>
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</tr>
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</tbody>
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</table>
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</div>
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Student_Name Age
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0 Ram 19
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1 Mohan 20
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2 Krishna 21
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3 Shivam 24
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```python
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```python
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record.describe() # It only display the results for numeric data
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print(record.describe()) # It only display the results for numeric data
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```
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>Age</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>count</th>
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<td>4.000000</td>
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</tr>
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<tr>
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<th>mean</th>
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<td>21.000000</td>
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</tr>
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<tr>
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<th>std</th>
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<td>2.160247</td>
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</tr>
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<tr>
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<th>min</th>
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<td>19.000000</td>
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</tr>
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<tr>
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<th>25%</th>
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<td>19.750000</td>
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</tr>
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<tr>
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<th>50%</th>
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<td>20.500000</td>
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</tr>
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<tr>
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<th>75%</th>
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<td>21.750000</td>
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</tr>
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<tr>
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<th>max</th>
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<td>24.000000</td>
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</tr>
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</tbody>
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</table>
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</div>
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Age
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count 4.000000
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mean 21.000000
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std 2.160247
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min 19.000000
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25% 19.750000
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50% 20.500000
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75% 21.750000
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max 24.000000
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#### 3. Use `.info()` to find information about the dataframe
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1 Age 4 non-null int64
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dtypes: int64(1), object(1)
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memory usage: 196.0+ bytes
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```python
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```

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