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How do you delete the header in a dataframe?


How do you delete the header in a dataframe?

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How do you delete the header in a dataframe?
Tag : r , By : David Marchant
Date : November 25 2020, 07:06 PM


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how to merge a multirows header of a pandas dataframe into a single cell header?


Tag : python , By : Moe Skeeto
Date : March 29 2020, 07:55 AM
I wish this helpful for you You can first select by loc, then replace NaN to empty string by fillna and apply join. If necessary remove first and last whitespaces by str.strip and then remove first rows by selecting df.loc[10:]:
df.columns = df.loc[5:9].fillna('').apply(' '.join).str.strip()

#if need monotonic index (0,1,2...) add reset index
print (df.loc[10:].reset_index(drop=True))
  Planting date YYYY.DDD Harvest date YYYY.DDD(kg/ha) Yield YYYY.DDD  \
0                1999.26                      2000.21        5669.46   
1                2000.27                      2001.22        10282.5   
2                2001.27                      2002.22        8210.09   

  Flowering date YYYY.DDD Maturity date YYYY.DDD Maturity date YYYY.DDD  \
0                 2000.14                2000.19                2000.19   
1                 2001.15                 2001.2                 2001.2   
2                 2002.15                 2002.2                 2002.2   

  Maturity date (kg/ha) Above ground biomass  
0               2000.19              11626.7  
1                2001.2                20565  
2                2002.2                16509  

Create dataframe ,subset from other dataframe with extra columns having blank values.There is a lookup table for header


Tag : python , By : Marc Dong
Date : March 29 2020, 07:55 AM
I wish this help you I have a dataframe , Use rename and reindex
In [124]: (df1.rename(columns=df2.set_index('Header2')['Header1'])
              .reindex(columns=df2['Header1'].values))
Out[124]:
   ticket JOB_ID  virtual
0       4    abc      NaN
1       6    cde      NaN
2       7    kde      NaN
3       8    mde      NaN
In [125]: df1
Out[125]:
   id  job   fname  lname
0   4  abc   james  frank
1   6  cde     bob  altin
2   7  kde   kevin  mchon
3   8  mde  george  fndes

In [126]: df2
Out[126]:
   Header1 Header2
0   ticket      id
1   JOB_ID     job
2  virtual     NaN

In [127]: df2.set_index('Header2')['Header1']
Out[127]:
Header2
id      ticket
job     JOB_ID
NaN    virtual
Name: Header1, dtype: object

In a multi header dataframe, how to select a column with one of the tuple values of the header, and the rest being anyth


Tag : python , By : Novi Indrayani
Date : March 29 2020, 07:55 AM
hop of those help? So I don't know if this is the "best" solution, but what did end up working for me was to first sort the headers using:
df.sort_index(axis=1, inplace=True)
df.loc[:, (slice(None), slice(None), "MyHeader3")]
df[[x for x in df.columns if 'header3' in x]]

Pandas- rename dataframe multilevel header according to the name of the first level header


Tag : python , By : Sergio Rudenko
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I have a dataframe like this : , I can not find a function can directly doing this so
df.columns=df.columns.values
df
Out[110]: 
   (X, a)  (X, b)  (Y, a)  (Y, b)
0       1       3       4       2
1       5       7       8       6
df.rename(columns={('Y', 'b'):('Y', 'b1')})
Out[111]: 
   (X, a)  (X, b)  (Y, a)  (Y, b1)
0       1       3       4        2
1       5       7       8        6
df=df.rename(columns={('Y', 'b'):('Y', 'b1')})
df.columns=pd.MultiIndex.from_tuples(df.columns)
df
Out[114]: 
   X     Y   
   a  b  a b1
0  1  3  4  2
1  5  7  8  6

Convert one level mixed header dataframe to vertical dataframe in Pandas


Tag : python , By : Magnus
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , You can create MultiIndex with columns with : by str.split with created index by non : columns before by DataFrame.set_index and then reshape by DataFrame.stack:
df = df.set_index('date')
df.columns = df.columns.str.split(':', expand=True)
df = df.stack().rename_axis(('date','district')).reset_index()
print (df)
   date district  price  ratio
0  2017       cy     14    0.3
1  2017       dc     12    0.1
2  2017       xc     11    0.1
3  2018       cy     15    0.6
4  2018       dc     14    0.2
5  2018       xc     12    0.7
6  2019       cy     16    0.8
7  2019       dc     13    0.5
8  2019       xc     13   -0.2
df = df.set_index('date')
df.columns = df.columns.str.split(':', expand=True)

lvl = pd.CategoricalIndex(df.columns.levels[1], 
                          ordered=True, 
                          categories=df.columns.get_level_values(1).drop_duplicates())
df.columns = df.columns.set_levels(lvl, level=1)

df = df.stack().sort_index(level=[1,0]).rename_axis(('date','district')).reset_index()
print (df)
   date district  price  ratio
0  2017       dc     12    0.1
1  2018       dc     14    0.2
2  2019       dc     13    0.5
3  2017       xc     11    0.1
4  2018       xc     12    0.7
5  2019       xc     13   -0.2
6  2017       cy     14    0.3
7  2018       cy     15    0.6
8  2019       cy     16    0.8
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