kopia lustrzana https://github.com/animator/learn-python
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Krishna Kaushik
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# Importing_and_Exporting_Data_in_Pandas
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>Created by Krishna Kaushik
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- **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.**
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- *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.*
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## 1. Importing from a Google sheet to a pandas dataframe
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*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*.
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*You could export it as a .csv file and then import it using ``pd.read_csv()``.*
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*In this case, the exported .csv file is called `Titanic.csv`*
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```python
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## Importing Titanic Data set
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import pandas as pd
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titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv")
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titanic_df
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```
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<div>
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<style scoped>
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.dataframe tbody tr th:only-of-type {
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vertical-align: middle;
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}
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.dataframe tbody tr th {
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vertical-align: top;
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}
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.dataframe thead th {
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text-align: right;
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}
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</style>
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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>pclass</th>
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<th>survived</th>
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<th>name</th>
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<th>sex</th>
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<th>age</th>
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<th>sibsp</th>
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<th>parch</th>
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<th>ticket</th>
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<th>fare</th>
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<th>cabin</th>
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<th>embarked</th>
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<th>boat</th>
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<th>body</th>
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<th>home.dest</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>1</td>
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<td>1</td>
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<td>Allen, Miss. Elisabeth Walton</td>
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<td>female</td>
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<td>29.00</td>
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<td>0</td>
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<td>0</td>
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<td>24160</td>
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<td>211.3375</td>
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<td>B5</td>
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<td>S</td>
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<td>2</td>
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<td>NaN</td>
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<td>St Louis, MO</td>
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</tr>
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<tr>
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<th>1</th>
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<td>1</td>
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<td>1</td>
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<td>Allison, Master. Hudson Trevor</td>
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<td>male</td>
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<td>0.92</td>
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<td>1</td>
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<td>2</td>
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<td>113781</td>
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<td>151.5500</td>
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<td>C22 C26</td>
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<td>S</td>
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<td>11</td>
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<td>NaN</td>
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<td>Montreal, PQ / Chesterville, ON</td>
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</tr>
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<tr>
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<th>2</th>
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<td>1</td>
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<td>0</td>
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<td>Allison, Miss. Helen Loraine</td>
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<td>female</td>
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<td>2.00</td>
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<td>1</td>
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<td>2</td>
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<td>113781</td>
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<td>151.5500</td>
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<td>C22 C26</td>
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<td>S</td>
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<td>NaN</td>
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<td>NaN</td>
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<td>Montreal, PQ / Chesterville, ON</td>
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</tr>
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<tr>
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<th>3</th>
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<td>1</td>
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<td>0</td>
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<td>Allison, Mr. Hudson Joshua Creighton</td>
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<td>male</td>
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<td>30.00</td>
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<td>1</td>
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<td>2</td>
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<td>113781</td>
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<td>151.5500</td>
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<td>C22 C26</td>
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<td>S</td>
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<td>NaN</td>
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<td>135.0</td>
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<td>Montreal, PQ / Chesterville, ON</td>
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</tr>
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<tr>
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<th>4</th>
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<td>1</td>
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<td>0</td>
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<td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>
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<td>female</td>
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<td>25.00</td>
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<td>1</td>
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<td>2</td>
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<td>113781</td>
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<td>151.5500</td>
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<td>C22 C26</td>
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<td>S</td>
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<td>NaN</td>
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<td>NaN</td>
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<td>Montreal, PQ / Chesterville, ON</td>
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</tr>
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<tr>
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<th>...</th>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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<td>...</td>
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</tr>
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<tr>
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<th>1304</th>
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<td>3</td>
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<td>0</td>
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<td>Zabour, Miss. Hileni</td>
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<td>female</td>
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<td>14.50</td>
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<td>1</td>
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<td>0</td>
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<td>2665</td>
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<td>14.4542</td>
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<td>NaN</td>
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<td>C</td>
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<td>NaN</td>
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<td>328.0</td>
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<td>NaN</td>
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</tr>
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<tr>
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<th>1305</th>
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<td>3</td>
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<td>0</td>
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<td>Zabour, Miss. Thamine</td>
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<td>female</td>
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<td>NaN</td>
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<td>1</td>
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<td>0</td>
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<td>2665</td>
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<td>14.4542</td>
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<td>NaN</td>
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<td>C</td>
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<td>NaN</td>
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<td>NaN</td>
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<td>NaN</td>
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</tr>
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<tr>
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<th>1306</th>
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<td>3</td>
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<td>0</td>
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<td>Zakarian, Mr. Mapriededer</td>
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<td>male</td>
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<td>26.50</td>
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<td>0</td>
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<td>0</td>
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<td>2656</td>
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<td>7.2250</td>
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<td>NaN</td>
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<td>C</td>
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<td>NaN</td>
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<td>304.0</td>
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<td>NaN</td>
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</tr>
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<tr>
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<th>1307</th>
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<td>3</td>
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<td>0</td>
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<td>Zakarian, Mr. Ortin</td>
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<td>male</td>
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<td>27.00</td>
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<td>0</td>
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<td>0</td>
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<td>2670</td>
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<td>7.2250</td>
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<td>NaN</td>
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<td>C</td>
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<td>NaN</td>
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<td>NaN</td>
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<td>NaN</td>
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</tr>
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<tr>
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<th>1308</th>
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<td>3</td>
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<td>0</td>
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<td>Zimmerman, Mr. Leo</td>
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<td>male</td>
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<td>29.00</td>
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<td>0</td>
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<td>0</td>
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<td>315082</td>
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<td>7.8750</td>
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<td>NaN</td>
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<td>S</td>
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<td>NaN</td>
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<td>NaN</td>
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<td>NaN</td>
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</tr>
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</tbody>
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</table>
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<p>1309 rows × 14 columns</p>
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</div>
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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.
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**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.**
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#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source.
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#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo .
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#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API``
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## 2. The Anatomy of DataFrame
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**Different functions use different labels for different things, and can get a little confusing.**
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- Rows are refer as ``axis=0``
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- columns are refer as ``axis=1``
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## 3. Exporting Data
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**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.**
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**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().**
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### Exporting a dataframe to a CSV
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**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.**
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```python
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#Export the titanic_df DataFrame to csv
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titanic_df.to_csv("exported_titanic.csv")
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```
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Running this will save a file called ``exported_titanic.csv`` to the current folder.
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