There's the pandas dataframe 'test_df'. My aim is to convert it to a dictionary. Therefore I run this:
id Name Gender Age
0 1 'Peter' 'M' 32
1 2 'Lara' 'F' 45
Therefore I run this:
test_dict = test_df.set_index('id').T.to_dict()
The output is this:
{1: {'Name': 'Peter', 'Gender': 'M', 'Age': 32}, 2: {'Name': 'Lara', 'Gender': 'F', 'Age': 45}}
Now, I want to choose only the 'Name' and 'Gender' columns as the values of dictionary's keys. I'm trying to modify the above script into sth like this:
test_dict = test_df.set_index('id')['Name']['Gender'].T.to_dict()
with no success!
Any suggestion please?!
You was very close, use subset of columns [['Name','Gender']]:
test_dict = test_df.set_index('id')[['Name','Gender']].T.to_dict()
print (test_dict)
{1: {'Name': 'Peter', 'Gender': 'M'}, 2: {'Name': 'Lara', 'Gender': 'F'}}
Also T is not necessary, use parameter orient='index':
test_dict = test_df.set_index('id')[['Name','Gender']].to_dict(orient='index')
print (test_dict)
{1: {'Name': 'Peter', 'Gender': 'M'}, 2: {'Name': 'Lara', 'Gender': 'F'}}
Related
Hi I am new to pandas/python and trying to read a txt file in pandas
I want to extract key, value pairs for each row.
Make the key as new column name and its respective value as values.
Input
data
{'Name': 'Tim', 'Class': 'Ninth', 'Hobbies' : 'Football'}
{'Name': 'Tom', 'Class': 'Ninth', 'Hobbies' : 'Football'}
{'Name': 'Jim', 'Class': 'Ninth', 'Hobbies' : 'Football'}
{'Name': 'John', 'Class': 'Ninth'}
Expected Output:
Name Class Hobbies
Tim Ninth Football
Tom Ninth Football
Jim Ninth Football
John Ninth NA
import pandas as pd
df1 = pd.read_csv('9data.txt',sep = '\t')
df1['Name'] = df1['data'].apply(lambda x : x.values()[1])
print(df1)
Error: AttributeError: 'str' object has no attribute 'values'
Is there any way in which i can do this in pandas ?
The way the data was being read, I could get it a new dataframe using eval(). This will iterate over each cell creating a new dataframe then concatenating them.
data='''data
{'Name': 'Tim', 'Class': 'Ninth', 'Hobbies' : 'Football'}
{'Name': 'Tom', 'Class': 'Ninth', 'Hobbies' : 'Football'}
{'Name': 'Jim', 'Class': 'Ninth', 'Hobbies' : 'Football'}
{'Name': 'John', 'Class': 'Ninth'}'''
df = pd.read_csv(io.StringIO(data), sep='\t', engine='python')
df1 = pd.concat([pd.json_normalize(eval(x)) for x in df['data']])
Output
Name Class Hobbies
0 Tim Ninth Football
0 Tom Ninth Football
0 Jim Ninth Football
0 John Ninth NaN
If you can get your data look like this, this is simpler method that Anurag Dabas alludes to. You might consider reading the file into a list first, then creating the dataframe, rather creating a dataframe from a dataframe.
datal = [{'Name': 'Tim', 'Class': 'Ninth', 'Hobbies' : 'Football'},
{'Name': 'Tom', 'Class': 'Ninth', 'Hobbies' : 'Football'},
{'Name': 'Jim', 'Class': 'Ninth', 'Hobbies' : 'Football'},
{'Name': 'John', 'Class': 'Ninth'}]
df = pd.DataFrame(datal)
df
fields = ['name', 'type', 'age']
df = pd.DataFrame(columns=fields)
item1 = {'name': 'john', type:'student', 'age': 21}
item2 = {'name': 'john', 'age': 21}
for item in items:
df = df.append(item, ignore_index=True)
I had thought only 'item1' would be able to be appended, not 'item2' since it has only 2 required fields. Is this normal?
Is there a smart pythonic way to parse a nested column in a pandas dataframe like this one to 3 different columns? So for example the column could look like this:
col1
[{'name': 'amount', 'value': 1}, {'name': 'frequency', 'value': 2}, {'name': 'freq_unit', 'value': 'month'}]
[{'name': 'amount', 'value': 3}, {'name': 'frequency', 'value': 1}, {'name': 'freq_unit', 'value': 'month'}]
And the expected result should be these 3 columns:
amount frequency freq_unit
1 2 month
3 1 month
That's just level 1. I have the level 2: What if the elements in the list still have the same names (amount, frequency and freq_unit) but the order could change? Could the code in the answer deal with this?
col1
[{'name': 'amount', 'value': 1}, {'name': 'frequency', 'value': 2}, {'name': 'freq_unit', 'value': 'month'}]
[{'name': 'amount', 'value': 3}, {'name': 'freq_unit', 'value': 'month'}, {'name': 'frequency', 'value': 1}]
Code for reproduce the data. Really look forward to see how the community would solve this. Thank you
data = {'col1':[[{'name': 'amount', 'value': 1}, {'name': 'frequency', 'value': 2}, {'name': 'freq_unit', 'value': 'month'}],
[{'name': 'amount', 'value': 3}, {'name': 'frequency', 'value': 1}, {'name': 'freq_unit', 'value': 'month'}]]}
df = pd.DataFrame(data)
A combination of list comprehension, itertools.chain, and collections.defaultdict could help out here:
from itertools import chain
from collections import defaultdict
data = defaultdict(list)
phase1 = [[(data["name"], data["value"])
for data in entry]
for entry in df.col1
]
phase1 = chain.from_iterable(phase1)
for key, value in phase1:
data[key].append(value)
pd.DataFrame(data)
amount frequency freq_unit
0 1 2 month
1 3 1 month
The above is verbose: #piRSquared's comment is much simpler, with a list comprehension:
pd.DataFrame([{x["name"]: x["value"] for x in lst} for lst in df.col1])
Another idea, but very unnecessary, is to use a list comprehension, combined with Pandas' string methods:
outcome = [(df.col1.str[num].str["value"]
.rename(df.col1.str[num].str["name"][0])
)
for num in range(df.col1.str.len()[0])
]
pd.concat(outcome, axis = 'columns')
#piRsquared's solution is the simplest, in my opinion.
You can write a function that will parse each cell in your Series and return a properly formatted Series and use apply to tuck the iteration away:
>>> def custom_parser(record):
... clean_record = {rec["name"]: rec["value"] for rec in record}
... return pd.Series(clean_record)
>>> df["col1"].apply(custom_parser)
amount frequency freq_unit
0 1 2 month
1 3 1 month
I would like to group my dataframe by one of the columns and then return a dictionary that has a list of all of the rows per column value. Is there a fast Pandas idiom for doing this?
Example:
test = pd.DataFrame({
'id': ['alice', 'bob', 'bob', 'charlie'],
'transaction_date': ['2020-01-01', '2020-01-01', '2020-01-02', '2020-01-02'],
'amount': [50.0, 10.0, 12.0, 13.0]
})
Desired output:
result = {
'alice': [Series(transaction_date='2020-01-01', amount=50.0)],
'bob': [Series(transaction_date='2020-01-01', amount=10.0), Series(transaction_date='2020-01-02', amount=12.0)],
'charlie': [Series(transaction_date='2020-01-02', amount=53.0)],
}
The following approaches do NOT work:
test.groupby('id').agg(list)
Returns a Dataframe where each column (amount and transaction_date) has a list of values, but that's not what I want. I want the result to be one list of rows / Pandas series per unique grouping column value ('id' value).
test.groupby('id').agg(list).to_dict():
{'amount': {'charlie': [13.0], 'bob': [10.0, 12.0], 'alice': [50.0]}, 'transaction_date': {'charlie': ['2020-01-02'], 'bob': ['2020-01-01', '2020-01-02'], 'alice': ['2020-01-01']}}
test.groupby('id').apply(list).to_dict():
{'charlie': ['amount', 'id', 'transaction_date'], 'bob': ['amount', 'id', 'transaction_date'], 'alice': ['amount', 'id', 'transaction_date']}
Use itertuples and zip,
import pandas as pd
test = pd.DataFrame({
'id': ['alice', 'bob', 'bob', 'charlie'],
'transaction_date': ['2020-01-01', '2020-01-01', '2020-01-02', '2020-01-02'],
'amount': [50.0, 10.0, 12.0, 13.0]
})
columns = ['transaction_date', 'amount']
grouped = (test
.groupby('id')[columns]
.apply(lambda x: list(x.itertuples(name='Series', index=False))))
print(dict(zip(grouped.index, grouped.values)))
{
'alice': [Series(transaction_date='2020-01-01', amount=50.0)],
'bob': [
Series(transaction_date='2020-01-01', amount=10.0),
Series(transaction_date='2020-01-02', amount=12.0)
],
'charlie': [Series(transaction_date='2020-01-02', amount=13.0)]
}
I have the following dataframe:
df = pd.DataFrame([{'name': 'a', 'label': 'false', 'score': 10},
{'name': 'a', 'label': 'true', 'score': 8},
{'name': 'c', 'label': 'false', 'score': 10},
{'name': 'c', 'label': 'true', 'score': 4},
{'name': 'd', 'label': 'false', 'score': 10},
{'name': 'd', 'label': 'true', 'score': 6},
])
I want to return names that have the "false" label score value higher than the score value of the "true" label with at least the double. In my example, it should return only the "c" name.
First you can pivot the data, and look at the ratio, filter what you want:
new_df = df.pivot(index='name',columns='label', values='score')
new_df[new_df['false'].div(new_df['true']).gt(2)]
output:
label false true
name
c 10 4
If you only want the label, you can do:
new_df.index[new_df['false'].div(new_df['true']).gt(2)].values
which gives
array(['c'], dtype=object)
Update: Since your data is result of orig_df.groupby().count(), you could instead do:
orig_df['label'].eq('true').groupby('name').mean()
and look at the rows with values <= 1/3.