I have a spreadsheet like this:
Locality 2005 2006 2007 2008 2009
ABBOTSFORD 427000 448000 602500 600000 638500
ABERFELDIE 534000 600000 735000 710000 775000
AIREYS INLET459000 440000 430000 517500 512500
I don’t want to manually swap the column with the row. Could it be possible to use pandas reading data to a list as this:
data['ABBOTSFORD']=[427000,448000,602500,600000,638500]
data['ABERFELDIE']=[534000,600000,735000,710000,775000]
data['AIREYS INLET']=[459000,440000,430000,517500,512500]
1
Yes, with pandas.DataFrame.set_index you can make 'Locality' your row index.
data.set_index('Locality', inplace=True)
If inplace=True is not provided, set_index returns the modified dataframe as a result.
Example:
> import pandas as pd
> df = pd.DataFrame([['ABBOTSFORD', 427000, 448000],
['ABERFELDIE', 534000, 600000]],
columns=['Locality', 2005, 2006])
> df
Locality 2005 2006
0 ABBOTSFORD 427000 448000
1 ABERFELDIE 534000 600000
> df.set_index('Locality', inplace=True)
> df
2005 2006
Locality
ABBOTSFORD 427000 448000
ABERFELDIE 534000 600000
> df.loc['ABBOTSFORD']
2005 427000
2006 448000
Name: ABBOTSFORD, dtype: int64
> df.loc['ABBOTSFORD'][2005]
427000
> df.loc['ABBOTSFORD'].values
array([427000, 448000])
> df.loc['ABBOTSFORD'].tolist()
[427000, 448000]
0
You can change the index as explained already using set_index.
You don’t need to manually swap rows with columns, there is a transpose (data.T) method in pandas that does it for you:
> df = pd.DataFrame([['ABBOTSFORD', 427000, 448000],
['ABERFELDIE', 534000, 600000]],
columns=['Locality', 2005, 2006])
> newdf = df.set_index('Locality').T
> newdf
Locality ABBOTSFORD ABERFELDIE
2005 427000 534000
2006 448000 600000
then you can fetch the dataframe column values and transform them to a list:
> newdf['ABBOTSFORD'].values.tolist()
[427000, 448000]
0
Another simple approach is to assign the column to the data frame index
data = {
'Locality': ['ABBOTSFORD', 'ABERFELDIE', 'AIREYS INLET'],
'2005': [427000, 534000, 459000 ],
'2006': [448000, 448000, 448000],
'2007': [602500, 602500, 602500],
'2008': [600000, 710000, 517500],
'2009': [638500, 775000, 512500]
}
df = pd.DataFrame(data)
# set the locality column as the index
df.index = df['Locality']
And if you no longer want the Locality column as a column, you can just drop it
df.drop('Locality', axis=1)
You’ll end up with
| 2005 | 2006 | 2007 | 2008 | 2009
Locality |-------------------------------------------
ABBOTSFORD | 427000 | 448000 | 602500 | 600000 | 638500
ABERFELDIE | 534000 | 448000 | 602500 | 710000 | 775000
AIREYS INLET | 459000 | 448000 | 602500 | 517500 | 512500
You can set the column index using index_col parameter available while reading from spreadsheet in Pandas.
Here is my solution:
-
Firstly, import pandas as pd:
import pandas as pd -
Read in filename using pd.read_excel() (if you have your data in a spreadsheet) and set the index to ‘Locality’ by specifying the index_col parameter.
df = pd.read_excel('testexcel.xlsx', index_col=0)At this stage if you get a ‘no module named xlrd’ error, install it using
pip install xlrd. -
For visual inspection, read the dataframe using
df.head()which will print the following output
-
Now you can fetch the values of the desired columns of the dataframe and print it
