I have a dict that holds computed values on different time lags, which means they start on different dates. For instance, the data I have may look like the following:
Date col1 col2 col3 col4 col5
01-01-15 5 12 1 -15 10
01-02-15 7 0 9 11 7
01-03-15 6 1 2 18
01-04-15 9 8 10
01-05-15 -4 7
01-06-15 -11 -1
01-07-15 6
Where each header is the key, and each column of values is the value for each key (I'm using a defaultdict(list)
for this). When I try to run pd.DataFrame.from_dict(d)
I understandably get an error stating that all arrays must be the same length. Is there an easy/trivial way to fill or pad the numbers so that the output would end up being the following dataframe?
Date col1 col2 col3 col4 col5
01-01-15 5 12 1 -15 10
01-02-15 7 0 9 11 7
01-03-15 NaN 6 1 2 18
01-04-15 NaN 9 8 10 NaN
01-05-15 NaN -4 NaN 7 NaN
01-06-15 NaN -11 NaN -1 NaN
01-07-15 NaN 6 NaN NaN NaN
Or will I have to do this manually with each list?
Here is the code to recreate the dictionary:
import pandas as pd
from collections import defaultdict
d = defaultdict(list)
d["Date"].extend([
"01-01-15",
"01-02-15",
"01-03-15",
"01-04-15",
"01-05-15",
"01-06-15",
"01-07-15"
]
d["col1"].extend([5, 7])
d["col2"].extend([12, 0, 6, 9, -4, -11, 6])
d["col3"].extend([1, 9, 1, 8])
d["col4"].extend([-15, 11, 2, 10, 7, -1])
d["col5"].extend([10, 7, 18])
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