how to convert string in to different columns python - python-3.x

I have csv file , which have one column and inside this column have string , string contains many values , i want to convert this string in muultiple columns
here is example data:
df = pd.DataFrame({'column1':[{'A':2,'B':3,'c':2}]})
print(df)
column1
0 {'A': 2, 'B': 3, 'c': 2}
1 {'A': 3, 'B': 5, 'c': 10}
i want output:
df = pd.DataFrame({'A':[2],'B':[3],'c':[2]})


try this:
pd.DataFrame([*df['column1'].apply(eval)])


First convert string that looks like dictionary to an actual dictionary
import json
my_dict = json.loads(column1)
# Gives you {'A': 2, 'B': 3, 'c': 2}
Then convert that dictionary to a dataframe:
pd.Dataframe([my_dict])

Related

How can I go through different dictionaries using nested loop?

sem1_credit = {'A': 4, 'B': 4, 'C': 3}
sem2_credit = {'D': 5, 'E': 1}
sem3_credit = {'F': 3}
e = 2
for j in range(e):
for i in 'sem'+str(j+1)+'_credit':
I wanted to use loop to access different dict. So I tried to create the dict name with concatenation using loop. But it doesn't work. Is there a way to work it out or is there some other way to approach dict without loops.

You can get a dictionary of the current local symbols table by calling locals(). So locals()['sem1_credit'] is essentially this sem1_credit.
From here, you can build a loop:
sem1_credit = {'A': 4, 'B': 4, 'C': 3}
sem2_credit = {'D': 5, 'E': 1}
sem3_credit = {'F': 3}
for idx in range(1, 4):
credits = locals()[f'sem{idx}_credit']
for key, credit in credits.items():
print(f"{key} {credit}")
Keep in mind that the range(num) generate numbers from 0 to num-1. So in your code, range(2) only generates 0 and 1.

How to do sorting in Python3 for a list dictionary inside?

I have a list of the dictionary as follows:
[{"A":5,"B":10},
{"A":6,"B":13},
{"A":10,"B":5}]
I want to this list in decending order on the value of B. The output should look like this:
[{"A":6,"B":13},
{"A":5,"B":10},
{"A":10,"B":5}]
How to do that?

You can sort lists by the results of applying a function to each element: https://docs.python.org/3.9/library/functions.html#sorted
>>> data = [{"A":5,"B":10},
... {"A":6,"B":13},
... {"A":10,"B":5}]
>>> sorted(data, key=lambda dct: dct["B"], reverse=True)
[{'A': 6, 'B': 13}, {'A': 5, 'B': 10}, {'A': 10, 'B': 5}]

How to calculate count of occurence of each value from a list column of Pandas efficiently?

I have a Pandas data frame, which looks like the following:
df =
col1
['a', 'b']
['d', 'c', 'a']
['b', 'f', 'a']
col1 is a list column, which contains strings. I want to calculate value counts of each element, which may occur in any of the lists in any row. Expected output is a dictionary, of counts of each value
Expected Output
df_dict = {'a': 3, 'b': 2, 'c': 1, 'd': 1, 'f': 1}
How to do this efficiently in 1 line preferably to make the code clean. Sorry, if it has been answered before.

With explode and value_counts:
df['col1'].explode().value_counts().to_dict()
Output:
{'a': 3, 'b': 2, 'd': 1, 'f': 1, 'c': 1}

Python Groupby keys list of dictionaries

I have the following for instance:
x = [{'A':1},{'A':1},{'A':2},{'B':1},{'B':1},{'B':2},{'B':3},{'C':1},{'D':1}]
and I would like to get a dictionary like this:
x = [{'A': [1,2], 'B': [1,2,3], 'C':[1], 'D': [1]}]
Do you have any idea how I could get this please?

You could use a collections.defaultdict of sets to collect unique values, then convert the final result to a dictionary with values as lists using a dict comprehension:
from collections import defaultdict
lst = [{'A':1},{'A':1},{'A':2},{'B':1},{'B':1},{'B':2},{'B':3},{'C':1},{'D':1}]
result = defaultdict(set)
for dic in lst:
for key, value in dic.items():
result[key].add(value)
print({key: list(value) for key, value in result.items()})
Output:
{'A': [1, 2], 'B': [1, 2, 3], 'C': [1], 'D': [1]}
Although its probably better to add your data directly to the defaultdict to begin with, instead of creating a list of singleton dictionaries(don't recommend this data structure) then converting the result.

Using dict.setdefault
Ex:
x = [{'A':1},{'A':1},{'A':2},{'B':1},{'B':1},{'B':2},{'B':3},{'C':1},{'D':1}]
res = {}
for i in x:
for k, v in i.items():
res.setdefault(k, set()).add(v)
#or res = [{k: list(v) for k, v in res.items()}]
print(res)
Output:
{'A': {1, 2}, 'B': {1, 2, 3}, 'C': {1}, 'D': {1}}

Dataframe to Dictionary [duplicate]

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 other columns in same row be values.
DataFrame:
ID A B C
0 p 1 3 2
1 q 4 3 2
2 r 4 0 9
Output should be like this:
Dictionary:
{'p': [1,3,2], 'q': [4,3,2], 'r': [4,0,9]}

The to_dict() method sets the column names as dictionary keys so you'll need to reshape your DataFrame slightly. Setting the 'ID' column as the index and then transposing the DataFrame is one way to achieve this.
to_dict() also accepts an 'orient' argument which you'll need in order to output a list of values for each column. Otherwise, a dictionary of the form {index: value} will be returned for each column.
These steps can be done with the following line:
>>> df.set_index('ID').T.to_dict('list')
{'p': [1, 3, 2], 'q': [4, 3, 2], 'r': [4, 0, 9]}
In case a different dictionary format is needed, here are examples of the possible orient arguments. Consider the following simple DataFrame:
>>> df = pd.DataFrame({'a': ['red', 'yellow', 'blue'], 'b': [0.5, 0.25, 0.125]})
>>> df
a b
0 red 0.500
1 yellow 0.250
2 blue 0.125
Then the options are as follows.
dict - the default: column names are keys, values are dictionaries of index:data pairs
>>> df.to_dict('dict')
{'a': {0: 'red', 1: 'yellow', 2: 'blue'},
'b': {0: 0.5, 1: 0.25, 2: 0.125}}
list - keys are column names, values are lists of column data
>>> df.to_dict('list')
{'a': ['red', 'yellow', 'blue'],
'b': [0.5, 0.25, 0.125]}
series - like 'list', but values are Series
>>> df.to_dict('series')
{'a': 0 red
1 yellow
2 blue
Name: a, dtype: object,
'b': 0 0.500
1 0.250
2 0.125
Name: b, dtype: float64}
split - splits columns/data/index as keys with values being column names, data values by row and index labels respectively
>>> df.to_dict('split')
{'columns': ['a', 'b'],
'data': [['red', 0.5], ['yellow', 0.25], ['blue', 0.125]],
'index': [0, 1, 2]}
records - each row becomes a dictionary where key is column name and value is the data in the cell
>>> df.to_dict('records')
[{'a': 'red', 'b': 0.5},
{'a': 'yellow', 'b': 0.25},
{'a': 'blue', 'b': 0.125}]
index - like 'records', but a dictionary of dictionaries with keys as index labels (rather than a list)
>>> df.to_dict('index')
{0: {'a': 'red', 'b': 0.5},
1: {'a': 'yellow', 'b': 0.25},
2: {'a': 'blue', 'b': 0.125}}

Should a dictionary like:
{'red': '0.500', 'yellow': '0.250', 'blue': '0.125'}
be required out of a dataframe like:
a b
0 red 0.500
1 yellow 0.250
2 blue 0.125
simplest way would be to do:
dict(df.values)
working snippet below:
import pandas as pd
df = pd.DataFrame({'a': ['red', 'yellow', 'blue'], 'b': [0.5, 0.25, 0.125]})
dict(df.values)

Follow these steps:
Suppose your dataframe is as follows:
>>> df
A B C ID
0 1 3 2 p
1 4 3 2 q
2 4 0 9 r
1. Use set_index to set ID columns as the dataframe index.
df.set_index("ID", drop=True, inplace=True)
2. Use the orient=index parameter to have the index as dictionary keys.
dictionary = df.to_dict(orient="index")
The results will be as follows:
>>> dictionary
{'q': {'A': 4, 'B': 3, 'D': 2}, 'p': {'A': 1, 'B': 3, 'D': 2}, 'r': {'A': 4, 'B': 0, 'D': 9}}
3. If you need to have each sample as a list run the following code. Determine the column order
column_order= ["A", "B", "C"] # Determine your preferred order of columns
d = {} # Initialize the new dictionary as an empty dictionary
for k in dictionary:
d[k] = [dictionary[k][column_name] for column_name in column_order]

Try to use Zip
df = pd.read_csv("file")
d= dict([(i,[a,b,c ]) for i, a,b,c in zip(df.ID, df.A,df.B,df.C)])
print d
Output:
{'p': [1, 3, 2], 'q': [4, 3, 2], 'r': [4, 0, 9]}

If you don't mind the dictionary values being tuples, you can use itertuples:
>>> {x[0]: x[1:] for x in df.itertuples(index=False)}
{'p': (1, 3, 2), 'q': (4, 3, 2), 'r': (4, 0, 9)}

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.
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},
... }
"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},
... }

Most of the answers do not deal with the situation where ID can exist multiple times in the dataframe. In case ID can be duplicated in the Dataframe df you want to use a list to store the values (a.k.a a list of lists), grouped by ID:
{k: [g['A'].tolist(), g['B'].tolist(), g['C'].tolist()] for k,g in df.groupby('ID')}

Dictionary comprehension & iterrows() method could also be used to get the desired output.
result = {row.ID: [row.A, row.B, row.C] for (index, row) in df.iterrows()}

df = pd.DataFrame([['p',1,3,2], ['q',4,3,2], ['r',4,0,9]], columns=['ID','A','B','C'])
my_dict = {k:list(v) for k,v in zip(df['ID'], df.drop(columns='ID').values)}
print(my_dict)
with output
{'p': [1, 3, 2], 'q': [4, 3, 2], 'r': [4, 0, 9]}

With this method, columns of dataframe will be the keys and series of dataframe will be the values.`
data_dict = dict()
for col in dataframe.columns:
data_dict[col] = dataframe[col].values.tolist()

DataFrame.to_dict() converts DataFrame to dictionary.
Example
>>> df = pd.DataFrame(
{'col1': [1, 2], 'col2': [0.5, 0.75]}, index=['a', 'b'])
>>> df
col1 col2
a 1 0.1
b 2 0.2
>>> df.to_dict()
{'col1': {'a': 1, 'b': 2}, 'col2': {'a': 0.5, 'b': 0.75}}
See this Documentation for details

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