# Reading CSV

Create CSV file with the following content and name it "AAPL.csv". It contains stock information for few days. You can get more data on [https://finance.yahoo.com/quote/AAPL/history?p=AAPL. ](https://finance.yahoo.com/quote/AAPL/history?p=AAPL)

```
Date,Open,High,Low,Close,Volume,Adj Close
2017-01-20,120.449997,120.449997,119.730003,120.00,29479900,120.00
2017-01-19,119.400002,120.089996,119.370003,119.779999,25295700,119.779999
2017-01-18,120.00,120.50,119.709999,119.989998,23644700,119.989998
2017-01-17,118.339996,120.239998,118.220001,120.00,34078600,120.00
2017-01-13,119.110001,119.620003,118.809998,119.040001,25938300,119.040001
2017-01-12,118.900002,119.300003,118.209999,119.25,27002400,119.25
2017-01-11,118.739998,119.93,118.599998,119.75,27418600,119.75
```

## Read and print all rows

Read all rows from CSV and print it out.

```
import pandas as pd

def test_run():
    df = pd.read_csv("data/AAPL.csv")
    print df

if __name__ == "__main__":
    test_run()
```

Here is the output when we run the code above.

```
Date        Open        High         Low       Close    Volume    Adj Close  
0  2017-01-20  120.449997  120.449997  119.730003  120.000000  29479900   120.000000  
1  2017-01-19  119.400002  120.089996  119.370003  119.779999  25295700   119.779999
2  2017-01-18  120.000000  120.500000  119.709999  119.989998  23644700   119.989998 
3  2017-01-17  118.339996  120.239998  118.220001  120.000000  34078600   120.000000
4  2017-01-13  119.110001  119.620003  118.809998  119.040001  25938300   119.040001
5  2017-01-12  118.900002  119.300003  118.209999  119.250000  27002400   119.250000
6  2017-01-11  118.739998  119.930000  118.599998  119.750000  27418600   119.750000
```

## Read header, tail or range

Read only header, first two rows or specified range.

```
import pandas as pd

def test_run():
    df = pd.read_csv("data/AAPL.csv")
    print df.head(n=1)
    print df.tail(n=2)
    print df[2:3]

if __name__ == "__main__":
    test_run()
```

Here is the output.

```
Date        Open        High         Low  Close    Volume  Adj Close
0  2017-01-20  120.449997  120.449997  119.730003  120.0  29479900      120.0

         Date        Open        High         Low       Close    Volume    Adj Close
5  2017-01-12  118.900002  119.300003  118.209999  119.250000  27002400   119.250000
6  2017-01-11  118.739998  119.930000  118.599998  119.750000  27418600   119.750000 

         Date   Open   High         Low       Close    Volume   Adj Close
2  2017-01-18  120.0  120.5  119.709999  119.989998  23644700  119.989998
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://ondrej-kvasnovsky-2.gitbook.io/handbook-of-hidden-data-scientist-python/chapter1/read-csv.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.