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Update Importing_and_Exporting_Data_in_Pandas.md

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@ -12,60 +12,31 @@
*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`*
*In this case, the exported .csv file is called `car-sales.csv`*
```python
## Importing Titanic Data set
import pandas as pd
titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv")
print(titanic_df)
car_sales_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv")
print(car_sales_df)
```
pclass survived name \
0 1 1 Allen, Miss. Elisabeth Walton
1 1 1 Allison, Master. Hudson Trevor
2 1 0 Allison, Miss. Helen Loraine
3 1 0 Allison, Mr. Hudson Joshua Creighton
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels)
... ... ... ...
1304 3 0 Zabour, Miss. Hileni
1305 3 0 Zabour, Miss. Thamine
1306 3 0 Zakarian, Mr. Mapriededer
1307 3 0 Zakarian, Mr. Ortin
1308 3 0 Zimmerman, Mr. Leo
sex age sibsp parch ticket fare cabin embarked boat \
0 female 29.00 0 0 24160 211.3375 B5 S 2
1 male 0.92 1 2 113781 151.5500 C22 C26 S 11
2 female 2.00 1 2 113781 151.5500 C22 C26 S NaN
3 male 30.00 1 2 113781 151.5500 C22 C26 S NaN
4 female 25.00 1 2 113781 151.5500 C22 C26 S NaN
... ... ... ... ... ... ... ... ... ...
1304 female 14.50 1 0 2665 14.4542 NaN C NaN
1305 female NaN 1 0 2665 14.4542 NaN C NaN
1306 male 26.50 0 0 2656 7.2250 NaN C NaN
1307 male 27.00 0 0 2670 7.2250 NaN C NaN
1308 male 29.00 0 0 315082 7.8750 NaN S NaN
body home.dest
0 NaN St Louis, MO
1 NaN Montreal, PQ / Chesterville, ON
2 NaN Montreal, PQ / Chesterville, ON
3 135.0 Montreal, PQ / Chesterville, ON
4 NaN Montreal, PQ / Chesterville, ON
... ... ...
1304 328.0 NaN
1305 NaN NaN
1306 304.0 NaN
1307 NaN NaN
1308 NaN NaN
[1309 rows x 14 columns]
Make Colour Odometer (KM) Doors Price
0 Toyota White 150043 4 $4,000.00
1 Honda Red 87899 4 $5,000.00
2 Toyota Blue 32549 3 $7,000.00
3 BMW Black 11179 5 $22,000.00
4 Nissan White 213095 4 $3,500.00
5 Toyota Green 99213 4 $4,500.00
6 Honda Blue 45698 4 $7,500.00
7 Honda Blue 54738 4 $7,000.00
8 Toyota White 60000 4 $6,250.00
9 Nissan White 31600 4 $9,700.00
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.
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/car-sales.csv) I uploaded it in the Datasets folder,you can use it from there.
You can also place the filename with its path in `pd.read_csv()`.
@ -92,12 +63,12 @@ You can also place the filename with its path in `pd.read_csv()`.
### Exporting a dataframe to a CSV
**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.**
**We haven't made any changes yet to the ``car_sales_df`` DataFrame but let's try to export it.**
```python
#Export the titanic_df DataFrame to csv
titanic_df.to_csv("exported_titanic.csv")
car_sales_df.to_csv("exported_car_sales_df.csv")
```
Running this will save a file called ``exported_titanic.csv`` to the current folder.