When sorting by a MultiIndex you need to contain the tuple describing the column inside a list*: In [11]: df.sort_values([(‘Group1’, ‘C’)], ascending=False) Out[11]: Group1 Group2 A B C A B C 2 5 6 9 1 0 0 1 1 0 3 2 5 7 3 7 0 2 0 3 5 * so as … Read more
Thanks to @cxrodgers’s comment, I think the fastest way to do this is: df.index = df.index.set_levels(df.index.levels[0].str.replace(‘ ‘, ”), level=0) Old, longer answer: I found that the list comprehension suggested by @Shovalt works but felt slow on my machine (using a dataframe with >10,000 rows). Instead, I was able to use .set_levels method, which was quite … Read more
df.reset_index(level=2, drop=True) Out[29]: A 1 1 8 3 9
I believe you’re looking for a groupby along the first axis. df.groupby(level=0, axis=1).sum() On older versions of pandas, this method also works: df.sum(level=0, axis=1) The level argument to sum implies grouping. df first bar baz foo second one two one two one two A 2 3 3 4 10 8 B 22 16 7 3 … Read more
You can make a new multi index based on the Cartesian product of the levels of the existing multi index. Then, re-index your data frame using the new index. new_index = pd.MultiIndex.from_product(df.index.levels) new_df = df.reindex(new_index) # Optional: convert missing values to zero, and convert the data back # to integers. See explanation below. new_df = … Read more
You can use multiIndex to give multiple columns with names for each level. Use MultiIndex.from_product() to make multiIndex from cartesian products of multiple iterables. header = pd.MultiIndex.from_product([[‘location1′,’location2’], [‘S1′,’S2′,’S3’]], names=[‘loc’,’S’]) df = pd.DataFrame(np.random.randn(5, 6), index=[‘a’,’b’,’c’,’d’,’e’], columns=header) Two levels will be loc and S. df loc location1 location2 S S1 S2 S3 S1 S2 S3 a -1.245988 … Read more
I think this will do it In [3]: df = DataFrame(dict(A = ‘foo’, B = ‘bar’, value = 1),index=range(5)).set_index([‘A’,’B’]) In [4]: df Out[4]: value A B foo bar 1 bar 1 bar 1 bar 1 bar 1 In [5]: df.to_csv(‘test.csv’) In [6]: !cat test.csv A,B,value foo,bar,1 foo,bar,1 foo,bar,1 foo,bar,1 foo,bar,1 In [7]: pd.read_csv(‘test.csv’,index_col=[0,1]) Out[7]: value … Read more
You need set display.multi_sparse to False: #if need temporary use option with pd.option_context(‘display.multi_sparse’, False): print (df) 0 Jim Betty 20 N/A 0.201643 50 N/A 0.201643 20 10 0.201643 20 30 0.201643 If this display option is required throughout a notebook, the option can be set once and for all as follows: # if permanent use … Read more
1. Update using Python 3.6+ use f-string formatting with list comprehension: df.columns = [f'{i}{j}’ for i, j in df.columns] 2. Use map and join: df.columns = df.columns.map(”.join) 3. If your columns has numeric datatypes, use map and format: df.columns = df.columns.map(‘{0[0]}{0[1]}’.format) Output: barone bartwo bazone baztwo fooone footwo foothree A 1 2 3 4 5 … Read more
The reset_index() is a pandas DataFrame method that will transfer index values into the DataFrame as columns. The default setting for the parameter is drop=False (which will keep the index values as columns). All you have to do call .reset_index() after the name of the DataFrame: df = df.reset_index()