df2md.md

Visualization of pd.DataFrame as a Markdown format

• Useful to generate markdown document
• [ NOTICE ] the output of the function is strongly influenced by markdown characters, such as |, *, ~. The output of the df2md should be used only for visualization, not as input to another function.

# Nice representation of dataframe in markdown
import pandas as pd
import copy
from IPython.display import Markdown, display
import warnings
warnings.filterwarnings(action='ignore')

def df2md(df, maxlen=20, indexname='(index)'):
    '''
    Nice Representation of Pandas DataFrame and Series in Markdown Format.
    
    Parameters
    ----------------
    df : pandas.DataFrame or pandas.Series
    maxlen : (int) maximum length of data at markdown output.
             data exceeding maxlen will be presented as ' ...'
    indexname : (str) name of index, to be displayed on markdown output.
                To avoid overriding column name, if conflicts, the '_' will be added in front of the indexname.
    '''
    
    _df = copy.deepcopy(df)
    
    if 'Series' in str(type(df)):
      _idx = _df.index
      _df = pd.DataFrame(data=_df, index=_idx)
      
    for col in _df.columns:
        _df[col] = _df[col].astype('str')
        if (_df[col].str.len()> maxlen).any() :
            _df[col].loc[_df[col].str.len() > maxlen] = _df[col].str.slice(stop=maxlen) + ' ...'

    if indexname in _df.columns:
        while(indexname in _df.columns):
            indexname='_'+indexname
        warnings.warn("The index name shouldn't overlap other column names. {} will be used instead.\nConsider changing the indexname parameter.".format(indexname), SyntaxWarning)
    _df.insert(0, indexname, df.index)

    fmt = ['---' for i in range(len(_df.columns))]
    df_fmt = pd.DataFrame([fmt], columns=_df.columns)
    df_formatted = pd.concat([df_fmt, _df])
    display(Markdown(df_formatted.to_csv(sep='|', index=False)))
    _df.drop(columns=indexname, axis=1, inplace=True)

Example: geodataframe of geopandas

# Defulat max. length = 20
df2md(seoul_h_dong_gdf.head(10))  

(index)	SGG_NM	DONG_NM	SGG_CODE	H_CODE	SEDAE	SEDAE_INGU	TOTAL_POP	MALE_POP	FEMALE_POP	65_OVER	geometry
0	종로구	사직동	11110	1111000	4414	2.14	9717	4467	5250	1708	POLYGON ((126.976888 ...
1	종로구	삼청동	11110	1111000	1414	2.06	3097	1459	1638	634	POLYGON ((126.982689 ...
2	종로구	부암동	11110	1111000	4325	2.39	10726	5160	5566	1748	POLYGON ((126.975851 ...
3	종로구	평창동	11110	1111000	7510	2.53	19163	9032	10131	3037	POLYGON ((126.975074 ...
4	종로구	무악동	11110	1111000	3024	2.75	8375	3921	4454	1279	POLYGON ((126.960673 ...
5	종로구	교남동	11110	1111000	4379	2.34	10363	4850	5513	1406	POLYGON ((126.969048 ...
6	종로구	가회동	11110	1111000	2085	2.17	4657	2194	2463	900	POLYGON ((126.989135 ...
7	종로구	종로1.2.3.4가동	11110	1111000	5378	1.41	8592	5045	3547	1865	POLYGON ((126.996499 ...
8	종로구	종로5·6가동	11110	1111000	3174	1.69	5690	3096	2594	1089	POLYGON ((127.010160 ...
9	종로구	이화동	11110	1111000	4462	1.85	8752	4075	4677	1302	POLYGON ((127.007332 ...

# For shorter max. length = 5
df2md(seoul_h_dong_gdf.head(10), maxlen=5)  

(index)	SGG_NM	DONG_NM	SGG_CODE	H_CODE	SEDAE	SEDAE_INGU	TOTAL_POP	MALE_POP	FEMALE_POP	65_OVER	geometry
0	종로구	사직동	11110	11110 ...	4414	2.14	9717	4467	5250	1708	POLYG ...
1	종로구	삼청동	11110	11110 ...	1414	2.06	3097	1459	1638	634	POLYG ...
2	종로구	부암동	11110	11110 ...	4325	2.39	10726	5160	5566	1748	POLYG ...
3	종로구	평창동	11110	11110 ...	7510	2.53	19163	9032	10131	3037	POLYG ...
4	종로구	무악동	11110	11110 ...	3024	2.75	8375	3921	4454	1279	POLYG ...
5	종로구	교남동	11110	11110 ...	4379	2.34	10363	4850	5513	1406	POLYG ...
6	종로구	가회동	11110	11110 ...	2085	2.17	4657	2194	2463	900	POLYG ...
7	종로구	종로1.2 ...	11110	11110 ...	5378	1.41	8592	5045	3547	1865	POLYG ...
8	종로구	종로5·6 ...	11110	11110 ...	3174	1.69	5690	3096	2594	1089	POLYG ...
9	종로구	이화동	11110	11110 ...	4462	1.85	8752	4075	4677	1302	POLYG ...

• Contrary: standard print() function

print(seoul_h_dong_gdf.head(10))

  SGG_NM      DONG_NM SGG_CODE   H_CODE  SEDAE  SEDAE_INGU  TOTAL_POP  \
0    종로구          사직동    11110  1111000   4414        2.14       9717   
1    종로구          삼청동    11110  1111000   1414        2.06       3097   
2    종로구          부암동    11110  1111000   4325        2.39      10726   
3    종로구          평창동    11110  1111000   7510        2.53      19163   
4    종로구          무악동    11110  1111000   3024        2.75       8375   
5    종로구          교남동    11110  1111000   4379        2.34      10363   
6    종로구          가회동    11110  1111000   2085        2.17       4657   
7    종로구  종로1.2.3.4가동    11110  1111000   5378        1.41       8592   
8    종로구      종로5·6가동    11110  1111000   3174        1.69       5690   
9    종로구          이화동    11110  1111000   4462        1.85       8752   

   MALE_POP  FEMALE_POP  65_OVER  \
0      4467        5250     1708   
1      1459        1638      634   
2      5160        5566     1748   
3      9032       10131     3037   
4      3921        4454     1279   
5      4850        5513     1406   
6      2194        2463      900   
7      5045        3547     1865   
8      3096        2594     1089   
9      4075        4677     1302   

                                            geometry  
0  POLYGON ((126.9768888427482 37.57565077944879,...  
1  POLYGON ((126.982689386493 37.5950655194224, 1...  
2  POLYGON ((126.9758511377569 37.59656422224408,...  
3  POLYGON ((126.9750746678809 37.63138628651299,...  
4  POLYGON ((126.960673532739 37.58079784202972, ...  
5  POLYGON ((126.9690483700185 37.56819441770833,...  
6  POLYGON ((126.9891359030894 37.59130668631862,...  
7  POLYGON ((126.9964997845193 37.5810225677299, ...  
8  POLYGON ((127.0101604483956 37.57156810157083,...  
9  POLYGON ((127.0073325396313 37.58320333921623,...