CODEWITHSUNDEEP
pandas
user3552178
user3552178

Reputation: 3013

How to create pandas dataframe with date as index

This is my code,

import plotly.plotly as py
import datetime
import pandas
import matplotlib.pyplot as plt
import pandas.io.data as pd


start = datetime.datetime(2016, 2, 1)
end   = datetime.datetime(2016, 2, 11)
#raw = pd.DataReader("tjx", "yahoo", start, end)
rawy = pd.DataReader("tjx", "yahoo", start, end)['Low']

print rawy
print "========================"

columns = ['Low']
newDf = pd.DataFrame(columns=columns)
newDf = newDf.fillna(0)

#newDf[0] = rawy[0]
#newDf[0:1] = rawy[0:1]
#newDf.loc[0] = rawy.loc[0]
newDf.loc[0] = rawy[0]
print newDf

The result is like this,

Date
2016-02-01    70.470001
2016-02-02    72.309998
2016-02-03    71.000000
2016-02-04    69.720001
2016-02-05    67.900002
2016-02-08    66.820000
2016-02-09    67.519997
2016-02-10    69.279999
2016-02-11    67.410004
Name: Low, dtype: float64
========================
         Low
0  70.470001

If you look at the last line of result, it's using 0 as index, not date from the original data frame. So how to correct this please ?

Upvotes: 3

Views: 5706

Answers (2)

Alexander
Alexander

Reputation: 109686

It is using zero as the index because that is the value you assigned to it. Try this instead.

newDf = pd.DataFrame(columns=columns)
>>> newDf
Empty DataFrame
Columns: [Low]
Index: []

newDf.ix[rawy.index[0]] = rawy[0]  # Or newDf.loc[rawy.index[0]] = rawy[0]
newDf.ix[rawy.index[1]] = rawy[1]

>>> newDf
                  Low
2016-02-01  70.470001
2016-02-02  72.309998

Upvotes: 1

Robert Rodkey
Robert Rodkey

Reputation: 423

If you want the index to come over, you've got to assign it. Here's two methods that seem to work:

>>> newDf = pd.DataFrame(data=[rawy[0]], index=[rawy.index[0]], columns=columns)
>>> newDf
                  Low
2016-02-01  70.470001

or

>>> newDf = pd.DataFrame(rawy.head(1))
>>> newDf
                   Low
 Date
 2016-02-01  70.470001

Upvotes: 2

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