# Joining CSVs

Create two CSV files with the following content and name it "AAPL.csv" and "GOOG.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)

```
AAPL.csv

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
```

```
GOOG.csv

Date,Open,High,Low,Close,Volume,Adj Close
2017-01-20,806.909973,806.909973,801.690002,805.02002,1645000,805.02002
2017-01-19,805.119995,809.47998,801.799988,802.174988,912000,802.174988
2017-01-18,805.809998,806.205017,800.98999,806.070007,1293300,806.070007
2017-01-17,807.080017,807.140015,800.369995,804.609985,1362100,804.609985
2017-01-13,807.47998,811.223999,806.690002,807.880005,1090100,807.880005
2017-01-12,807.140015,807.390015,799.169983,806.359985,1351000,806.359985
2017-01-11,805.00,808.150024,801.369995,807.909973,1057900,807.909973
```

First, load data only certain data range. That is done by defining what column should be used for indexing, see `index_col` *parameter in* `read_csv` *function. Then we provide other parameters for read\_csv function (check* [*read\_csv documentation*](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) *to find out what are these parameters doing).*

Then we rename 'Adj Close' column, so it is not in conflict when joining other CSV that contains column of the same name.

Finally, join the APPL CSV into our new data frame df1. Drop rows that contain just NaN values. And then we could iterate through other CSV files and join them into df1 data frame.

```python
import pandas as pd

def test_run():
    start_date = '2017-01-01'
    end_date = '2017-01-30'

    dates = pd.date_range(start_date, end_date)

    df1 = pd.DataFrame(index=dates)

    dfAPPL = pd.read_csv("data/APPL.csv",
                        index_col='Date',
                        parse_dates=True,
                        usecols=['Date', 'Adj Close'],
                        na_values=['nan']
                        )
    dfAPPL = dfAPPL.rename(columns={'Adj Close': 'APPL'})

    df1 = df1.join(dfAPPL, how='inner') # left join by default
    df1 = df1.dropna() # drop rows with NaN values

    symbols = ['GOOG', 'IBM']
    for symbol in symbols:
        df_temp = pd.read_csv("data/{}.csv".format(symbol),
                              index_col='Date',
                              parse_dates=True,
                              usecols=['Date', 'Adj Close'],
                              na_values='nan')
        df_temp = df_temp.rename(columns={'Adj Close': symbol})
        df1 = df1.join(df_temp)
    print df1

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


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