我有一個(gè)巨大的 json 文件，它有一個(gè)鍵調(diào)用類型（犯罪類型）、日期和時(shí)間（犯罪日期）和位置（地址或緯度和經(jīng)度）以及其他帶有值的鍵。我最感興趣的是計(jì)算犯罪率最高的天數(shù)，計(jì)算出現(xiàn)最多的呼叫類型，以及出現(xiàn)最多的位置，位置可以通過家庭住址或經(jīng)緯度配對(duì)來衡量。Python 可能是最好的。具有超過 350K 數(shù)據(jù)行的 JSON 上有超過 350 種調(diào)用類型。所以每當(dāng)你看到一個(gè)新的調(diào)用類型時(shí)，它應(yīng)該為此創(chuàng)建一個(gè)新變量并跟蹤它我嘗試迭代將它像列表一樣拋出但有問題。當(dāng)我的代碼為 62 mb 時(shí)，我如何附加到我的代碼，我應(yīng)該鏈接到一個(gè)文件嗎？這是一個(gè)數(shù)據(jù)示例[{"A": "incident_num", "B": "date_time", "C": "day", "D": "stno", "E": "stdir1", "F": "StreetName", "G": "streettype", "H": "FullAddress", "I": "call_type", "J": "disposition", "K": "beat", "L": "priority", "M": "lat", "N": "long"},{"A": "P17060024503", "B": "6/14/2017 21:54", "C": "4", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1151", "J": "O", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},{"A": "P17030051227", "B": "3/29/2017 22:24", "C": "4", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1016", "J": "A", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},{"A": "P17060004814", "B": "6/3/2017 18:04", "C": "7", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1016", "J": "A", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},{"A": "P17030029336", "B": "3/17/2017 10:57", "C": "6", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1151", "J": "OT", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},{"A": "P17030005412", "B": "3/3/2017 23:45", "C": "6", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "911P", "J": "CAN", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"},我只想要統(tǒng)計(jì)數(shù)據(jù)，例如撥打的每種電話類型以及撥打的時(shí)間，或者哪個(gè)地點(diǎn)犯罪率最高，哪一天犯罪率最高等等。
2 回答
三國紛爭(zhēng)
TA貢獻(xiàn)1804條經(jīng)驗(yàn) 獲得超7個(gè)贊
使用pandas：
import pandas as pd
raw_df = pd.DataFrame(data)
df = raw_df.rename(columns=raw_df.iloc[0]).drop(0)
df
輸出：
incident_num date_time day stno stdir1 StreetName ... call_type disposition beat priority lat long
1 P17060024503 6/14/2017 21:54 4 10 14TH ... 1151 O 521 2 32.7054489 -117.1518696
2 P17030051227 3/29/2017 22:24 4 10 14TH ... 1016 A 521 2 32.7054489 -117.1518696
3 P17060004814 6/3/2017 18:04 7 10 14TH ... 1016 A 521 2 32.7054489 -117.1518696
4 P17030029336 3/17/2017 10:57 6 10 14TH ... 1151 OT 521 2 32.7054489 -117.1518696
5 P17030005412 3/3/2017 23:45 6 10 15TH ... 911P CAN 521 2 32.7057215 -117.1503498
6 P17020016091 2/10/2017 8:23 6 10 15TH ... AU2 W 521 2 32.7057215 -117.1503498
7 P17040017368 4/11/2017 4:57 3 10 15TH ... 5150 CAN 521 2 32.7057215 -117.1503498
8 P17030048050 3/28/2017 6:30 3 10 15TH ... 1146 K 521 32.7057215 -117.1503498
9 P17060037341 6/22/2017 10:19 5 10 15TH ... 242 K 521 1 32.7057215 -117.1503498
10 P17060008467 6/5/2017 19:27 2 10 15TH ... 5150 K 521 2 32.7057215 -117.1503498
您可以運(yùn)行的查詢示例：
>>> df['call_type'].value_counts()
5150 2
1016 2
1151 2
242 1
911P 1
AU2 1
1146 1
喵喵時(shí)光機(jī)
TA貢獻(xiàn)1846條經(jīng)驗(yàn) 獲得超7個(gè)贊
迭代 json 文件并將所需字段存儲(chǔ)在 assosiatve 數(shù)組中。您可以對(duì)其進(jìn)行操作。
如果數(shù)據(jù)具有固定的列和結(jié)構(gòu)，您可以將其存儲(chǔ)在 MySql 等數(shù)據(jù)庫中，并且您可以通過簡(jiǎn)單的查詢輕松執(zhí)行所需的操作。
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