I have the following Pandas DataFrame, but am having trouble updating a column header value, or easily accessing the header values (for example, for plotting a time at the (lon,lat) location from the header).
df = pd.DataFrame(columns = ["id0", "id1", "id2"])
df.loc[2012]= [24, 25, 26]
df.loc[2013]= [28, 28, 29]
df.loc[2014]= [30, 31, 32]
df.columns = pd.MultiIndex.from_arrays([df.columns, [66,67,68], [110,111,112]],
names=['id','lat','lon'])
Which then looks like this:
>>> df
id id0 id1 id2
lat 66 67 68
lon 110 111 112
2012 24.0 25.0 26.0
2013 28.0 28.0 29.0
2014 30.0 31.0 32.0
I'd like to be able to adjust the latitude or longitude for df['id0'], or plot(df.ix[2014]) but at (x,y) location based on (lon,lat).
You can use df.columns.get_level_values('lat') in order to get the index object. This returns a copy of the index, so you cannot extend this approach to modify the coordinates inplace.
However, you can access the levels directly and modify them inplace using this workaround.
import pandas as pd
import numpy as np
df = pd.DataFrame(columns = ["id0", "id1", "id2"])
df.loc[2012]= [24, 25, 26]
df.loc[2013]= [28, 28, 29]
df.loc[2014]= [30, 31, 32]
df.columns = pd.MultiIndex.from_arrays([df.columns, [66,67,68], [110,111,112]],
names=['id','lat','lon'])
ids = df.columns.get_level_values('id')
id_ = 'id0'
column_position = np.where(ids.values == id_)
new_lat = 90
new_lon = 0
df.columns._levels[1].values[column_position] = new_lat
df.columns._levels[2].values[column_position] = new_lon
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