Merge data frames based on date

Question

I want to merge 2 data frames.

The first data frame looks like:

Date    Value

1968-04-01  38.0

1968-04-02  37.6

1968-04-03  37.7

1968-04-04  36.7

1968-04-05  37.2

1968-04-08  37.0

1968-04-09  37.25

1968-04-10  37.6

The second data frame looks like:

1991-06-21  4.44

1991-06-22  4.39

1991-06-24  4.39

1991-06-25  4.37

1991-06-26  4.41

1991-06-27  4.36

Both data sets go up to the present day.

How do I create a new data frame that starts the data in 1991 since that is where the second data set starts? And has a column for each price.

I tried:

df_all_rows = pd.concat([df1, df2])

but this just puts one set of data below the other one.

I guess currently the data does not go up to the present day. But how can I just create a new data frame with only dates that both series have?

Here is what I have:

import numpy as np

import pandas as pd

import pickle

import quandl

from datetime import datetime

df1=quandl.get("BUNDESBANK/BBK01_WT5511", authtoken="6F92X3NEV8DdrhAc_d5_")

df2=quandl.get("PERTH/SLVR_USD_D", authtoken="6F92X3NEV8DdrhAc_d5_")

df2=df2.dropna()

T1 = pd.merge(df1, df2, on=df1.index, how='inner')

Answer 1

First, add a bracket after ==x, second, to filter on a pandas data frame you need to add .loc before the bracket

wine_classes = [wine_data_frame.loc[wine_data_frame['class'] == x] for x in range(3)]

This will give you a list of data frames

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