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I have a dataframe that looks like this:
desc item type1 date1 type2 date2 type3 date3
0 this foo1 A 9/1 B 9/2 C 9/3
1 this foo2 D 9/4 E 9/5 F 9/6
How do I get it to look like:
desc item type date
0 this foo1 A 9/1
1 this foo1 B 9/2
2 this foo1 C 9/3
3 this foo2 D 9/4
4 this foo2 E 9/5
5 this foo2 F 9/6
?
882
set_option() to expand the number of displayed columns in a DataFrame. Call pandas. set_option("display. max_columns", width) with width as an integer to set the max_columns displayed to the desired width .
By use + operator simply you can combine/merge two or multiple text/string columns in pandas DataFrame. Note that when you apply + operator on numeric columns it actually does addition instead of concatenation.
Use pandas. concat() to concatenate/merge two or multiple pandas DataFrames across rows or columns. When you concat() two pandas DataFrames on rows, it creates a new Dataframe containing all rows of two DataFrames basically it does append one DataFrame with another.
In this tutorial, we will learn the Python pandas DataFrame.expanding () method. This is one of the window methods of pandas and it provides expanding transformations. It returns a window sub-classed for the particular operation.
You can use the following syntax to combine two text columns into one in a pandas DataFrame: df ['new_column'] = df ['column1'] + df ['column2'] If one of the columns isn’t already a string, you can convert it using the astype (str) command: df ['new_column'] = df ['column1'].astype(str) + df ['column2'] And you can use the following syntax to ...
import pandas as pd #import x2 = x.days.apply (lambda x: pd.Series (x)).unstack () #make an unstackeded series, x2 x.drop ('days', axis = 1).join (pd.DataFrame (x2.reset_index (level=0, drop=True))) #drop the days column, join to the x2 series
Check with wide_to_long
out = pd.wide_to_long(df.reset_index(), ['type','date'], i ='index', j = 'drop').reset_index(drop=True)
out
Out[127]:
type date
0 A 9/1
1 B 9/2
2 C 9/3
For your updated question, the same concept still applies, you just do not need to reset the index, since item is unique:
pd.wide_to_long(df, stubnames=['type','date'], i='item',j='drop').droplevel(-1).reset_index()
item type date
0 foo1 A 9/1
1 foo2 D 9/4
2 foo1 B 9/2
3 foo2 E 9/5
4 foo1 C 9/3
5 foo2 F 9/6
You can also use .melt on two dataframes by passing a list to value_vars using list comprehension if a column contains type or date. Then, you can merge these two dataframes on the index:
df = pd.merge(df.melt(id_vars='item', value_vars=[col for col in df.columns if 'type' in col], value_name='type')[['item','type']],
df.melt(id_vars='item', value_vars=[col for col in df.columns if 'date' in col], value_name='date')['date'],
how='left', left_index=True, right_index=True).sort_values('type')
df
Out[1]:
item type date
0 foo1 A 9/1
2 foo1 B 9/2
4 foo1 C 9/3
1 foo2 D 9/4
3 foo2 E 9/5
5 foo2 F 9/6
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