字典的字节列表转换为DataFrame [英] Bytes list of dictionary convert to DataFrame

问题描述

我对字节和熊猫很陌生，

I am pretty new to bytes and pandas,

我有这样的数据

但不确定如何转换为数据框.

But not sure how to convert into a dataframe.

data=[b"{'metricValue': 5.0, 'appMetadata':{'index': 'cfs_planum_metrics_debug_86188', 'host': 'iaasn00041949', 'job': 'splunk_scraper'}, 'timestampEpochSecond': 1544651948897, 'metricName': 'splunk_logs_tstats_count_per_min', 'metricType': 'count', 'metricTags': {'source': '/opt/splunk/etc/apps/PlanumComputeMetrics/bin/logs/DECOInstance2.log', 'query_timestamp': '2018-12-12T16:43:40.000-05:00'}}", b"{'metricValue': 4.0, 'appMetadata': {'index': 'cfs_digital_88082', 'host': 'dgt01p01tx5l046', 'job': 'splunk_scraper'}, 'timestampEpochSecond': 1544651948462, 'metricName': 'splunk_logs_tstats_count_per_min', 'metricType': 'count', 'metricTags': {'source': '/logs/apache24inst0/httpds0_access.log', 'query_timestamp': '2018-12-12T16:43:50.000-05:00'}}"] 

感谢您的帮助

推荐答案

给出数据的结构

{
    'metricValue': 5.0,
    'appMetadata': {
        'index': 'cfs_planum_metrics_debug_86188',
        'host': 'iaasn00041949',
        'job': 'splunk_scraper'
    },
    'timestampEpochSecond': 1544651948897,
    'metricName': 'splunk_logs_tstats_count_per_min',
    'metricType': 'count',
    'metricTags': {
        'source': '/opt/splunk/etc/apps/PlanumComputeMetrics/bin/logs/DECOInstance2.log',
        'query_timestamp': '2018-12-12T16:43:40.000-05:00'
    }
}, {
    'metricValue': 4.0,
    'appMetadata': {
        'index': 'cfs_digital_88082',
        'host': 'dgt01p01tx5l046',
        'job': 'splunk_scraper'
    },
    'timestampEpochSecond': 1544651948462,
    'metricName': 'splunk_logs_tstats_count_per_min',
    'metricType': 'count',
    'metricTags': {
        'source': '/logs/apache24inst0/httpds0_access.log',
        'query_timestamp': '2018-12-12T16:43:50.000-05:00'
    }
}

可以

# Convert your data from list of bytes into a list of strings
list_of_string = list(map(lambda d: d.decode('utf-8'), data))

# Parse the list of strings into a list of dictionaries
from ast import literal_eval
list_of_dicts = list(map(literal_eval, list_of_string))

# Convert the list to a DataFrame
df = pd.DataFrame(list_of_dicts)

# Convert appMetadata to a DataFrame
app_metadata = pd.concat(df['appMetadata']
                         .apply(pd.DataFrame.from_dict, orient='index')
                         .apply(lambda x: x.T)
                         .to_dict()).reset_index(level=1, drop=True)

# Convert metricTags to a DataFrame
metric_tags = pd.concat(df['metricTags']
                        .apply(pd.DataFrame.from_dict, orient='index')
                        .apply(lambda x: x.T)
                        .to_dict()).reset_index(level=1, drop=True)

# Join everything back to the original DataFrame
df = df.join(app_metadata).drop('appMetadata', axis=1)
df = df.join(metric_tags).drop('metricTags', axis=1)

或者，或者

# Flatten the dictionaries
def dict_flatten(d):
    for key in d:
        val = d[key]
        if isinstance(val, dict):
            for sub_key in val:
                yield sub_key, val[sub_key]
        else:
            yield key, val

flat_dicts = list(map(dict, map(dict_flatten, list_of_dicts)))

# Convert the list of flattened dictionaries to a DataFrame
df = pd.DataFrame(flat_dicts)

两者都会(最多按列顺序)

both resulting in (up to the order of the columns)

                         metricName metricType  metricValue  timestampEpochSecond       ...                      query_timestamp                           index             host             job
0  splunk_logs_tstats_count_per_min      count          5.0         1544651948897       ...        2018-12-12T16:43:40.000-05:00  cfs_planum_metrics_debug_86188    iaasn00041949  splunk_scraper
1  splunk_logs_tstats_count_per_min      count          4.0         1544651948462       ...        2018-12-12T16:43:50.000-05:00               cfs_digital_88082  dgt01p01tx5l046  splunk_scraper

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