I have a pandas.dataframe named 'df' with the following format:
| group_name | Positive_Sentiment | Negative_Sentiment |
|---|---|---|
| group1 | helpful, great support | slow customer service, weak interface, bad management |
I would like to convert this dataframe to a JSON file with the following format:
[{
"Group Name": "group1",
"Postive Sentiment": [
"helpful",
"great support"
],
"Negative Sentiment": [
"slow customer service",
"weak interface",
"bad management"
]
}
]
So far I have used this:
import json
b = []
for i in range(len(df)):
x={}
x['Group Name']=df.iloc[i]['group_name']
x['Positive Sentiment']= [df.iloc[i]['Positive_Sentiment']]
x['Negative Sentiment']= [df.iloc[i]['Negative_Sentiment']]
b.append(x)
##Export
with open('AnalysisResults.json', 'w') as f:
json.dump(b, f, indent = 2)
This results in:
[{
"Group Name": "group1",
"Postive Sentiment": [
"helpful,
great support"
],
"Negative Sentiment": [
"slow customer service,
weak interface,
bad UX"
]
}
]
You can see it is quite close. The crucial difference is the double-quotes around the ENTIRE contents of each row (e.g., "helpful, great support") instead of each comma-separated string in the row (e.g., "helpful", "great support"). I would like double-quotes around each string.
You can apply split(",") to your columns:
from io import StringIO
import pandas as pd
import json
inp = StringIO("""group_name Positive_Sentiment Negative_Sentiment
group1 helpful, great support slow customer service, weak interface, bad management
group2 great, good support interface meeeh, bad management""")
df = pd.read_csv(inp, sep="\s{2,}")
def split_and_strip(sentiment):
[x.strip() for x in sentiment.split(",")]
df["Positive_Sentiment"] = df["Positive_Sentiment"].apply(split_and_strip)
df["Negative_Sentiment"] = df["Negative_Sentiment"].apply(split_and_strip)
print(json.dumps(df.to_dict(orient="record"), indent=4))
# to save directly to a file:
with open("your_file.json", "w+") as f:
json.dump(df.to_dict(orient="record"), f, indent=4)
Output:
[
{
"group_name": "group1",
"Positive_Sentiment": [
"helpful",
"great support"
],
"Negative_Sentiment": [
"slow customer service",
"weak interface",
"bad management"
]
},
{
"group_name": "group2",
"Positive_Sentiment": [
"great",
"good support"
],
"Negative_Sentiment": [
"interface meeeh",
"bad management"
]
}
]
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