- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.**

- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.*

*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*.

*You could export it as a .csv file and then import it using ``pd.read_csv()``.*

*In this case, the exported .csv file is called `Titanic.csv`*

import pandas as pd

titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv")

titanic_df

<div>

<style scoped>

.dataframe tbody tr th:only-of-type {

vertical-align: middle;

}

.dataframe tbody tr th {

vertical-align: top;

}

.dataframe thead th {

text-align: right;

}

</style>

<table border="1" class="dataframe">

<thead>

<tr style="text-align: right;">

<th></th>

<th>pclass</th>

<th>survived</th>

<th>name</th>

<th>sex</th>

<th>age</th>

<th>sibsp</th>

<th>parch</th>

<th>ticket</th>

<th>fare</th>

<th>cabin</th>

<th>embarked</th>

<th>boat</th>

<th>body</th>

<th>home.dest</th>

</tr>

</thead>

<tbody>

<tr>

<th>0</th>

<td>1</td>

<td>1</td>

<td>Allen, Miss. Elisabeth Walton</td>

<td>female</td>

<td>29.00</td>

<td>0</td>

<td>0</td>

<td>24160</td>

<td>211.3375</td>

<td>B5</td>

<td>S</td>

<td>2</td>

<td>NaN</td>

<td>St Louis, MO</td>

</tr>

<tr>

<th>1</th>

<td>1</td>

<td>1</td>

<td>Allison, Master. Hudson Trevor</td>

<td>male</td>

<td>0.92</td>

<td>1</td>

<td>2</td>

<td>113781</td>

<td>151.5500</td>

<td>C22 C26</td>

<td>S</td>

<td>11</td>

<td>NaN</td>

<td>Montreal, PQ / Chesterville, ON</td>

</tr>

<tr>

<th>2</th>

<td>1</td>

<td>0</td>

<td>Allison, Miss. Helen Loraine</td>

<td>female</td>

<td>2.00</td>

<td>1</td>

<td>2</td>

<td>113781</td>

<td>151.5500</td>

<td>C22 C26</td>

<td>S</td>

<td>NaN</td>

<td>NaN</td>

<td>Montreal, PQ / Chesterville, ON</td>

</tr>

<tr>

<th>3</th>

<td>1</td>

<td>0</td>

<td>Allison, Mr. Hudson Joshua Creighton</td>

<td>male</td>

<td>30.00</td>

<td>1</td>

<td>2</td>

<td>113781</td>

<td>151.5500</td>

<td>C22 C26</td>

<td>S</td>

<td>NaN</td>

<td>135.0</td>

<td>Montreal, PQ / Chesterville, ON</td>

</tr>

<tr>

<th>4</th>

<td>1</td>

<td>0</td>

<td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>

<td>female</td>

<td>25.00</td>

<td>1</td>

<td>2</td>

<td>113781</td>

<td>151.5500</td>

<td>C22 C26</td>

<td>S</td>

<td>NaN</td>

<td>NaN</td>

<td>Montreal, PQ / Chesterville, ON</td>

</tr>

<tr>

<th>...</th>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

<td>...</td>

</tr>

<tr>

<th>1304</th>

<td>3</td>

<td>0</td>

<td>Zabour, Miss. Hileni</td>

<td>female</td>

<td>14.50</td>

<td>1</td>

<td>0</td>

<td>2665</td>

<td>14.4542</td>

<td>NaN</td>

<td>C</td>

<td>NaN</td>

<td>328.0</td>

<td>NaN</td>

</tr>

<tr>

<th>1305</th>

<td>3</td>

<td>0</td>

<td>Zabour, Miss. Thamine</td>

<td>female</td>

<td>NaN</td>

<td>1</td>

<td>0</td>

<td>2665</td>

<td>14.4542</td>

<td>NaN</td>

<td>C</td>

<td>NaN</td>

<td>NaN</td>

<td>NaN</td>

</tr>

<tr>

<th>1306</th>

<td>3</td>

<td>0</td>

<td>Zakarian, Mr. Mapriededer</td>

<td>male</td>

<td>26.50</td>

<td>0</td>

<td>0</td>

<td>2656</td>

<td>7.2250</td>

<td>NaN</td>

<td>C</td>

<td>NaN</td>

<td>304.0</td>

<td>NaN</td>

</tr>

<tr>

<th>1307</th>

<td>3</td>

<td>0</td>

<td>Zakarian, Mr. Ortin</td>

<td>male</td>

<td>27.00</td>

<td>0</td>

<td>0</td>

<td>2670</td>

<td>7.2250</td>

<td>NaN</td>

<td>C</td>

<td>NaN</td>

<td>NaN</td>

<td>NaN</td>

</tr>

<tr>

<th>1308</th>

<td>3</td>

<td>0</td>

<td>Zimmerman, Mr. Leo</td>

<td>male</td>

<td>29.00</td>

<td>0</td>

<td>0</td>

<td>315082</td>

<td>7.8750</td>

<td>NaN</td>

<td>S</td>

<td>NaN</td>

<td>NaN</td>

<td>NaN</td>

</tr>

</tbody>

</table>

<p>1309 rows × 14 columns</p>

</div>

The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv) I uploaded it you can use it from there.

**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.**

**Different functions use different labels for different things, and can get a little confusing.**

- Rows are refer as ``axis=0``

- columns are refer as ``axis=1``

**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.**

**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().**

**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.**

titanic_df.to_csv("exported_titanic.csv")

Running this will save a file called ``exported_titanic.csv`` to the current folder.