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‎contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md

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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/Datasets/Titanic.csv")
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print(titanic_df)
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```
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pclass survived name \
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0 1 1 Allen, Miss. Elisabeth Walton
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1 1 1 Allison, Master. Hudson Trevor
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2 1 0 Allison, Miss. Helen Loraine
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3 1 0 Allison, Mr. Hudson Joshua Creighton
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4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels)
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... ... ... ...
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1304 3 0 Zabour, Miss. Hileni
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1305 3 0 Zabour, Miss. Thamine
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1306 3 0 Zakarian, Mr. Mapriededer
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1307 3 0 Zakarian, Mr. Ortin
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1308 3 0 Zimmerman, Mr. Leo
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sex age sibsp parch ticket fare cabin embarked boat \
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0 female 29.00 0 0 24160 211.3375 B5 S 2
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1 male 0.92 1 2 113781 151.5500 C22 C26 S 11
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2 female 2.00 1 2 113781 151.5500 C22 C26 S NaN
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3 male 30.00 1 2 113781 151.5500 C22 C26 S NaN
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4 female 25.00 1 2 113781 151.5500 C22 C26 S NaN
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... ... ... ... ... ... ... ... ... ...
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1304 female 14.50 1 0 2665 14.4542 NaN C NaN
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1305 female NaN 1 0 2665 14.4542 NaN C NaN
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1306 male 26.50 0 0 2656 7.2250 NaN C NaN
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1307 male 27.00 0 0 2670 7.2250 NaN C NaN
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1308 male 29.00 0 0 315082 7.8750 NaN S NaN
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body home.dest
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0 NaN St Louis, MO
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1 NaN Montreal, PQ / Chesterville, ON
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2 NaN Montreal, PQ / Chesterville, ON
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3 135.0 Montreal, PQ / Chesterville, ON
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4 NaN Montreal, PQ / Chesterville, ON
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... ... ...
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1304 328.0 NaN
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1305 NaN NaN
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1306 304.0 NaN
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1307 NaN NaN
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1308 NaN NaN
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[1309 rows x 14 columns]
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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/Datasets/Titanic.csv) I uploaded it in the Datasets folder,you can use it from there.
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You can also place the filename with its path in `pd.read_csv()`.
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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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