将多个 JSON 文件转换为 CSV Python
在本文中，我们将学习如何在Python中将多个 JSON 文件转换为 CSV 文件。在此之前，请回忆一些术语：
- JSON 文件：JSON 文件可能是以 JavaScript Object Notation (JSON) 格式存储简单数据结构和对象的文件，该格式可能是标准的数据交换格式。它主要用于在互联网应用程序和服务器之间传输数据。
- CSV 文件：CSV 可以是逗号分隔值文件，它允许以表格格式保存数据。 CSV 看起来就像一个普通的电子表格，但带有.CSV扩展名。 CSV 文件通常用于几乎所有电子表格程序，如 Microsoft Excel 或 Google 电子表格。
要从多个 JSON 文件形成一个 CSV 文件，我们必须使用嵌套的 json 文件，将数据框展平或将 json 文件加载到数据框的形式，将它们连接/合并/加入以形成一个数据框（至少应该有一列在所有 json 文件中都相同），最后将此数据帧转换为 CSV 文件。借助下面给出的示例，可以理解给定任务的完整过程：
示例 1：如果所有列都匹配
在此示例中，我们将加载两个 json 文件，将一个连接到另一个并转换为 CSV 文件。用于此的 json 文件是：
文件1.json
{
"ID":{
"0":23,
"1":43,
"2":12,
"3":13,
"4":67,
"5":89
},
"Name":{
"0":"Ram",
"1":"Deep",
"2":"Yash",
"3":"Aman",
"4":"Arjun",
"5":"Aditya"
},
"Marks":{
"0":89,
"1":97,
"2":45,
"3":78,
"4":56,
"5":76
},
"Grade":{
"0":"B",
"1":"A",
"2":"F",
"3":"C",
"4":"E",
"5":"C"
}
}
文件2.json
{
"ID":{
"0":90,
"1":56,
"2":34,
"3":96,
"4":45
},
"Name":{
"0":"Akash",
"1":"Chalsea",
"2":"Divya",
"3":"Sajal",
"4":"Shubham"
},
"Marks":{
"0":81,
"1":87,
"2":100,
"3":89,
"4":78
},
"Grade":{
"0":"B",
"1":"B",
"2":"A",
"3":"B",
"4":"C"
}
}
第 1 步：在 pandas 数据框的帮助下加载 json 文件。
第 2 步：将数据帧连接成一个数据帧。
第 3 步：将串联的数据帧转换为 CSV 文件。
带有结果的完整代码如下所示：
代码：
Python3
# importing packages
import pandas as pd
# load json file using pandas
df1 = pd.read_json('file1.json')
# view data
print(df1)
# load json file using pandas
df2 = pd.read_json('file2.json')
# view data
print(df2)
# use pandas.concat method
df = pd.concat([df1,df2])
# view the concatenated dataframe
print(df)
# convert dataframe to csv file
df.to_csv("CSV.csv",index=False)
# load the resultant csv file
result = pd.read_csv("CSV.csv")
# and view the data
print(result)Python3
# importing packages
import pandas as pd
# load json file using pandas
df1 = pd.read_json('file3.json')
# view data
print(df1)
# load json file using pandas
df2 = pd.read_json('file4.json')
# view data
print(df2)
# use pandas.merge method
df_inner = pd.merge(df1, df2, how='inner', left_on=[
'ID', 'Name'], right_on=['ID', 'Name'])
df_outer = pd.merge(df1, df2, how='outer', left_on=[
'ID', 'Name'], right_on=['ID', 'Name'])
df_left = pd.merge(df1, df2, how='left', left_on=[
'ID', 'Name'], right_on=['ID', 'Name'])
df_right = pd.merge(df1, df2, how='right', left_on=[
'ID', 'Name'], right_on=['ID', 'Name'])
# convert dataframe to csv file
df_inner.to_csv("CSV_inner.csv", index=False)
df_outer.to_csv("CSV_outer.csv", index=False)
df_left.to_csv("CSV_left.csv", index=False)
df_right.to_csv("CSV_right.csv", index=False)
# load the resultant csv file
result_inner = pd.read_csv("CSV_inner.csv")
result_outer = pd.read_csv("CSV_outer.csv")
result_left = pd.read_csv("CSV_left.csv")
result_right = pd.read_csv("CSV_right.csv")
# and view the data
print(result_outer)
print(result_inner)
print(result_left)
print(result_right)Python3
# importing packages
import pandas as pd
import json
# load json file using json.load
with open('file5.json') as file:
data = json.load(file)
# view data
print(data)
# form the dataframe
df = pd.DataFrame(data['tickets'])
# view dataframe
print(df)
# flattern the dataframe and remove unnecessary columns
for i, item in enumerate(df['Location']):
df['location_city'] = dict(df['Location'])[i]['City']
df['location_state'] = dict(df['Location'])[i]['State']
for i, item in enumerate(df['hobbies']):
df['hobbies_{}'.format(i)] = dict(df['hobbies'])[i]
df = df.drop({'Location', 'hobbies'}, axis=1)
# view dataframe
print(df)
# convert dataframe to csv file
df.to_csv("CSV.csv", index=False)
# load the resultant csv file
result = pd.read_csv("CSV.csv")
# and view the data
print(result)输出：
ID Name Marks Grade
0 23 Ram 89 B
1 43 Deep 97 A
2 12 Yash 45 F
3 13 Aman 78 C
4 67 Arjun 56 E
5 89 Aditya 76 C
ID Name Marks Grade
0 90 Akash 81 B
1 56 Chalsea 87 B
2 34 Divya 100 A
3 96 Sajal 89 B
4 45 Shubham 78 C
ID Name Marks Grade
0 23 Ram 89 B
1 43 Deep 97 A
2 12 Yash 45 F
3 13 Aman 78 C
4 67 Arjun 56 E
5 89 Aditya 76 C
0 90 Akash 81 B
1 56 Chalsea 87 B
2 34 Divya 100 A
3 96 Sajal 89 B
4 45 Shubham 78 C
ID Name Marks Grade
0 23 Ram 89 B
1 43 Deep 97 A
2 12 Yash 45 F
3 13 Aman 78 C
4 67 Arjun 56 E
5 89 Aditya 76 C
6 90 Akash 81 B
7 56 Chalsea 87 B
8 34 Divya 100 A
9 96 Sajal 89 B
10 45 Shubham 78 C
示例 2：如果某些列匹配
在这个例子中，我们将加载两个 json 文件，合并它们并转换为 CSV 文件。用于此的 json 文件是：
文件3.json
{
"ID":{
"0":23,
"1":43,
"2":12,
"3":13,
"4":67,
"5":89
},
"Name":{
"0":"Ram",
"1":"Deep",
"2":"Yash",
"3":"Aman",
"4":"Arjun",
"5":"Aditya"
},
"Marks":{
"0":89,
"1":97,
"2":45,
"3":78,
"4":56,
"5":76
}
}
文件4.json
{
"ID":{
"0":23,
"1":43,
"2":12,
"3":67,
"4":89
},
"Name":{
"0":"Ram",
"1":"Deep",
"2":"Yash",
"3":"Arjun",
"4":"Aditya"
},
"Grade":{
"0":"B",
"1":"A",
"2":"F",
"3":"E",
"4":"C"
}
}
第 1 步：在 pandas 数据框的帮助下加载 json 文件。
第 2 步：通过不同的方法将数据帧合并为内/外/左/右连接。
第 3 步：将合并后的数据框转换为 CSV 文件。
带有结果的完整代码如下所示：
代码：
蟒蛇3
# importing packages
import pandas as pd
# load json file using pandas
df1 = pd.read_json('file3.json')
# view data
print(df1)
# load json file using pandas
df2 = pd.read_json('file4.json')
# view data
print(df2)
# use pandas.merge method
df_inner = pd.merge(df1, df2, how='inner', left_on=[
'ID', 'Name'], right_on=['ID', 'Name'])
df_outer = pd.merge(df1, df2, how='outer', left_on=[
'ID', 'Name'], right_on=['ID', 'Name'])
df_left = pd.merge(df1, df2, how='left', left_on=[
'ID', 'Name'], right_on=['ID', 'Name'])
df_right = pd.merge(df1, df2, how='right', left_on=[
'ID', 'Name'], right_on=['ID', 'Name'])
# convert dataframe to csv file
df_inner.to_csv("CSV_inner.csv", index=False)
df_outer.to_csv("CSV_outer.csv", index=False)
df_left.to_csv("CSV_left.csv", index=False)
df_right.to_csv("CSV_right.csv", index=False)
# load the resultant csv file
result_inner = pd.read_csv("CSV_inner.csv")
result_outer = pd.read_csv("CSV_outer.csv")
result_left = pd.read_csv("CSV_left.csv")
result_right = pd.read_csv("CSV_right.csv")
# and view the data
print(result_outer)
print(result_inner)
print(result_left)
print(result_right)
输出：
ID Name Marks
0 23 Ram 89
1 43 Deep 97
2 12 Yash 45
3 13 Aman 78
4 67 Arjun 56
5 89 Aditya 76
ID Name Grade
0 23 Ram B
1 43 Deep A
2 12 Yash F
3 67 Arjun E
4 89 Aditya C
ID Name Marks Grade
0 23 Ram 89 B
1 43 Deep 97 A
2 12 Yash 45 F
3 13 Aman 78 NaN
4 67 Arjun 56 E
5 89 Aditya 76 C
ID Name Marks Grade
0 23 Ram 89 B
1 43 Deep 97 A
2 12 Yash 45 F
3 67 Arjun 56 E
4 89 Aditya 76 C
ID Name Marks Grade
0 23 Ram 89 B
1 43 Deep 97 A
2 12 Yash 45 F
3 13 Aman 78 NaN
4 67 Arjun 56 E
5 89 Aditya 76 C
ID Name Marks Grade
0 23 Ram 89 B
1 43 Deep 97 A
2 12 Yash 45 F
3 67 Arjun 56 E
4 89 Aditya 76 C
示例 3：如果给出了嵌套的 json 文件
在本例中，我们将加载嵌套的 json 文件，将其展平，然后转换为 CSV 文件。用于此的 json 文件是：
文件5.json
{
"tickets":[
{
"Name": "Liam",
"Location": {
"City": "Los Angeles",
"State": "CA"
},
"hobbies": [
"Piano",
"Sports"
],
"year" : 1985,
"teamId" : "ATL",
"playerId" : "barkele01",
"salary" : 870000
},
{
"Name": "John",
"Location": {
"City": "Los Angeles",
"State": "CA"
},
"hobbies": [
"Music",
"Running"
],
"year" : 1985,
"teamId" : "ATL",
"playerId" : "bedrost01",
"salary" : 550000
}
],
"count": 2
}
第 1 步：借助 json.load() 方法加载嵌套的 json 文件。
第 2 步：使用 pandas 方法展平不同的列值。
第 3 步：将扁平化的数据框转换为 CSV 文件。
对两个嵌套文件重复上述步骤，然后按照示例 1 或示例 2 进行转换。要转换单个嵌套的 json 文件，请遵循下面给出的方法。
带有结果的完整代码如下所示：
代码：
蟒蛇3
# importing packages
import pandas as pd
import json
# load json file using json.load
with open('file5.json') as file:
data = json.load(file)
# view data
print(data)
# form the dataframe
df = pd.DataFrame(data['tickets'])
# view dataframe
print(df)
# flattern the dataframe and remove unnecessary columns
for i, item in enumerate(df['Location']):
df['location_city'] = dict(df['Location'])[i]['City']
df['location_state'] = dict(df['Location'])[i]['State']
for i, item in enumerate(df['hobbies']):
df['hobbies_{}'.format(i)] = dict(df['hobbies'])[i]
df = df.drop({'Location', 'hobbies'}, axis=1)
# view dataframe
print(df)
# convert dataframe to csv file
df.to_csv("CSV.csv", index=False)
# load the resultant csv file
result = pd.read_csv("CSV.csv")
# and view the data
print(result)
输出：
{‘tickets’: [{‘Name’: ‘Liam’, ‘Location’: {‘City’: ‘Los Angeles’, ‘State’: ‘CA’}, ‘hobbies’: [‘Piano’, ‘Sports’], ‘year’: 1985, ‘teamId’: ‘ATL’, ‘playerId’: ‘barkele01’, ‘salary’: 870000}, {‘Name’: ‘John’, ‘Location’: {‘City’: ‘Los Angeles’, ‘State’: ‘CA’}, ‘hobbies’: [‘Music’, ‘Running’], ‘year’: 1985, ‘teamId’: ‘ATL’, ‘playerId’: ‘bedrost01’, ‘salary’: 550000}], ‘count’: 2}
Location Name hobbies playerId \
0 {‘City’: ‘Los Angeles’, ‘State’: ‘CA’} Liam [Piano, Sports] barkele01
1 {‘City’: ‘Los Angeles’, ‘State’: ‘CA’} John [Music, Running] bedrost01
salary teamId year
0 870000 ATL 1985
1 550000 ATL 1985
Name playerId salary teamId year location_city location_state \
0 Liam barkele01 870000 ATL 1985 Los Angeles CA
1 John bedrost01 550000 ATL 1985 Los Angeles CA
hobbies_0 hobbies_1
0 Piano Music
1 Sports Running
Name playerId salary teamId year location_city location_state \
0 Liam barkele01 870000 ATL 1985 Los Angeles CA
1 John bedrost01 550000 ATL 1985 Los Angeles CA
hobbies_0 hobbies_1
0 Piano Music
1 Sports Running