I have a pandas DataFrame, with a column called positions
, that includes string values with the syntax of the following examples:
[{'y': 49, 'x': 44}, {'y': 78, 'x': 31}]
[{'y': 1, 'x': 63}, {'y': 0, 'x': 23}]
[{'y': 54, 'x': 9}, {'y': 78, 'x': 3}]
I want to create four new columns in my pandas DataFrame, y_start
, x_start
, y_end
, x_end
, that are extractions of only the numbers.
Eg for the example of the first row, my new columns would have the following values:
y_start
= 49
x_start
= 44
y_end
= 78
x_end
= 31
To summarise, I am looking to extract just the first, second, third, and four occurrence of numbers and save these to individual columns.
ast.literal_eval
pandas.DataFrame
constructor, because it's faster than using .apply(pd.Series)
pandas.json_normalize
, .rename
the columns, and .concat
them together.converters
parameter with .read_csv
.
df = pd.read_csv('data.csv', converters={'str_column': literal_eval})
import pandas as pd
from ast import literal_eval
# dataframe
data = {'data': ["[{'y': 49, 'x': 44}, {'y': 78, 'x': 31}]", "[{'y': 1, 'x': 63}, {'y': 0, 'x': 23}]", "[{'y': 54, 'x': 9}, {'y': 78, 'x': 3}]"]}
df = pd.DataFrame(data)
# convert the strings in the data column to dicts
df.data = df.data.apply(literal_eval)
# separate the strings into separate columns
df[['start', 'end']] = pd.DataFrame(df.data.tolist(), index=df.index)
# use json_normalize to convert the dicts to separate columns and join the dataframes with concat
cleaned = pd.concat([pd.json_normalize(df.start).rename(lambda x: f'{x}_start', axis=1), pd.json_normalize(df.end).rename(lambda x: f'{x}_end', axis=1)], axis=1)
# display(cleaned)
y_start x_start y_end x_end
0 49 44 78 31
1 1 63 0 23
2 54 9 78 3
Convert string to object:
import ast
df['positions'] = df['positions'].apply(ast.literal_eval)
This is one way:
df1 = pd.DataFrame.from_records(pd.DataFrame.from_records(df.positions)[0]).rename(columns={"x":"x_start", "y":"y_start"})
df2 = pd.DataFrame.from_records(pd.DataFrame.from_records(df.positions)[1]).rename(columns={"x":"x_end", "y":"y_end"})
df_new = pd.concat([df1, df2], axis=1)
another, a little more concise:
df1 = pd.DataFrame(df.positions.to_list())[0].apply(pd.Series).rename(columns={"x":"x_start", "y":"y_start"})
df2 = pd.DataFrame(df.positions.to_list())[1].apply(pd.Series).rename(columns={"x":"x_end", "y":"y_end"})
df_new = pd.concat([df1, df2], axis=1)
I don't know offhand the time or memory performance of how these methods compare.
output (either method):
y_start x_start y_end x_end
0 49 44 78 31
1 1 63 0 23
2 54 9 78 3
Not so clean but the working way is to write a custom function and apply lambda assuming that all your rows follow the same pattern as provided in your question:
### custom function
def startEndxy(x):
x = x.split(':')
return x[1].split(',')[0].replace(' ', ''), x[2].split('},')[0].replace(' ', ''), x[3].split(',')[0].replace(' ', ''), x[4].split('}')[0].replace(' ', '')
### columns creations
df['y_start'] = df['positions'].apply(lambda x: startEndxy(x)[0])
df['x_start'] = df['positions'].apply(lambda x: startEndxy(x)[1])
df['y_end'] = df['positions'].apply(lambda x: startEndxy(x)[2])
df['x_end'] = df['positions'].apply(lambda x: startEndxy(x)[3])
It should give you this output: Output
First reconstruct your series
df = pd.DataFrame(df['position'].tolist()).rename(columns={0: 'starts', 1:'ends'})
starts ends
0 {'y': 54, 'x': 9} {'y': 78, 'x': 3}
1 {'y': 1, 'x': 63} {'y': 0, 'x': 23}
2 {'y': 54, 'x': 9} {'y': 78, 'x': 3}
Then assign the start and end columns
starts = pd.DataFrame(df['starts'].tolist()).rename(columns={'y': 'y_start', 'x': 'x_start'})
ends = pd.DataFrame(df['end'].tolist()).rename(columns={'y': 'y_start', 'x': 'x_start'})
df = pd.concat([starts, ends], axis=1)
y_start x_start y_end x_end
0 54 9 78 3
1 1 63 0 23
2 54 9 78 3
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