Convert a Pandas DataFrame to a dictionary

Question

I have a DataFrame with four columns. I want to convert this DataFrame to a python dictionary. I want the elements of first column be keys and the elements of o

Answer 1

For my use (node names with xy positions) I found @user4179775's answer to the most helpful / intuitive:

import pandas as pd

df = pd.read_csv('glycolysis_nodes_xy.tsv', sep='\t')

df.head()
    nodes    x    y
0  c00033  146  958
1  c00031  601  195
...

xy_dict_list=dict([(i,[a,b]) for i, a,b in zip(df.nodes, df.x,df.y)])

xy_dict_list
{'c00022': [483, 868],
 'c00024': [146, 868],
 ... }

xy_dict_tuples=dict([(i,(a,b)) for i, a,b in zip(df.nodes, df.x,df.y)])

xy_dict_tuples
{'c00022': (483, 868),
 'c00024': (146, 868),
 ... }

---

Addendum

I later returned to this issue, for other, but related, work. Here is an approach that more closely mirrors the [excellent] accepted answer.

node_df = pd.read_csv('node_prop-glycolysis_tca-from_pg.tsv', sep='\t')

node_df.head()
   node  kegg_id kegg_cid            name  wt  vis
0  22    22       c00022   pyruvate        1   1
1  24    24       c00024   acetyl-CoA      1   1
...

Convert Pandas dataframe to a [list], {dict}, {dict of {dict}}, ...

Per accepted answer:

node_df.set_index('kegg_cid').T.to_dict('list')

{'c00022': [22, 22, 'pyruvate', 1, 1],
 'c00024': [24, 24, 'acetyl-CoA', 1, 1],
 ... }

node_df.set_index('kegg_cid').T.to_dict('dict')

{'c00022': {'kegg_id': 22, 'name': 'pyruvate', 'node': 22, 'vis': 1, 'wt': 1},
 'c00024': {'kegg_id': 24, 'name': 'acetyl-CoA', 'node': 24, 'vis': 1, 'wt': 1},
 ... }

In my case, I wanted to do the same thing but with selected columns from the Pandas dataframe, so I needed to slice the columns. There are two approaches.

1. Directly:

(see: Convert pandas to dictionary defining the columns used fo the key values)

node_df.set_index('kegg_cid')[['name', 'wt', 'vis']].T.to_dict('dict')

{'c00022': {'name': 'pyruvate', 'vis': 1, 'wt': 1},
 'c00024': {'name': 'acetyl-CoA', 'vis': 1, 'wt': 1},
 ... }

2. "Indirectly:" first, slice the desired columns/data from the Pandas dataframe (again, two approaches),

node_df_sliced = node_df[['kegg_cid', 'name', 'wt', 'vis']]

or

node_df_sliced2 = node_df.loc[:, ['kegg_cid', 'name', 'wt', 'vis']]

that can then can be used to create a dictionary of dictionaries

node_df_sliced.set_index('kegg_cid').T.to_dict('dict')

{'c00022': {'name': 'pyruvate', 'vis': 1, 'wt': 1},
 'c00024': {'name': 'acetyl-CoA', 'vis': 1, 'wt': 1},
 ... }