pd.concat()pd.concat([df1,df2])
| Stacks df1 and df2 | pd.concat([df1,df2],keys=['1','2'])
| Creates a label (key) for each df | pd.concat([df1,df2], join='inner')
| Default join is "inner join" | df1.append(df2)
| Does the same thing as pd.concat() |
Multi-indexingpd.MultiIndex.from_arrays([['a','a','b','b'],[1,2,1,2]])
| pd.MultiIndex.from_tuples([('a',1),('a',2),('b',1),('b',2)])
| pd.MultiIndex.from_product([['a','b'],[1,2]])
| df.sort_index()
| Orders index in ascending order | df.reset_index(name='')
| Turn the index labels into cols |
All three methods of pd.MultiIndex give:
MultiIndex(
[('a', 1),('a', 2),('b', 1),('b', 2)]
)` | | Joinspd.merge(df1,df2)
| pd.merge(df1,df2,left_on="a",right_on="b")
| Merges data using "a" and "b" as keys | pd.merge(df1,df2,how='inner')
| Default is "outer join" | pd.merge(df1,df2, on="a",suffixes=["_L","_R"])
| When multiple cols have same index and name, gives suffix "_L" and "_R" |
Aggregation and Groupingdf.mean(axis='columns')
| Calculates the mean across cols | df.groupby('key').sum()
| Gives sum for each key | df.groupby('key')['a'].sum()
| Gives sum for all rows in col "a" | df.groupby('key')['a'].describe()
| Gives summary stats | df.groupby('key').transform(lambda x:x-x.mean())
| Applies lambda function as aggregation function | df.groupby('key').apply(function)
| Applies "function" as aggregation function |
| | Pivot Tablesdf.pivot_table(index='a',columns='col',aggfunc='sum')
| Groups by index and gives the sum of column "a" | df.pivot_table(index='a',columns='col',aggfunc='sum').plot()
| Plots values using "a" as x-axis and sum(col) as y-axis |
Pivot tables do the same thing as using groupby with aggregation functions. The main difference is that they are a cleaner way of using multiple aggregation functions at once for a single grouping |
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