blocks|key|3474055|text|我刚刚用这个代码段(R内核)来转换它：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3474056|library(foreign)
write.table(read.spss("C:/Users/Nicola/Desktop/Relevant+projects+activities+ACF/BRACED+Final+Evaluation/Evaluations/CSI_compil_2017.sav"),+file="from_sav_data.csv",+quote+=+FALSE,+sep+=+",")|code-block|syntax|javascript|3474057|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I just managed to convert it with this snippet (R Kernel):

<pre><code>library(foreign)
write.table(read.spss("C:/Users/Nicola/Desktop/Relevant projects activities ACF/BRACED Final Evaluation/Evaluations/CSI_compil_2017.sav"), file="from_sav_data.csv", quote = FALSE, sep = ",")
</code></pre>

blocks|key|3474041|text|请参阅以下答案：https://stackoverflow.com/a/20873154/5999386|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3474042|简而言之，使用import+pandas.rpy.common+as+com来使用R功能将.sav文件解析为Pandas的数据帧。|style|CODE|3474043|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/20873154/5999386^0|8|18|0|0|7|V|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@$A|U|B|V|F|G]]|9|@]|C|$]]|$1|H|3|-4|5|6|7|W|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|O]]]]

Please see the following answer :
<a href="https://stackoverflow.com/a/20873154/5999386">https://stackoverflow.com/a/20873154/5999386</a>

In short, using <code>import pandas.rpy.common as com</code> to use R capabilities to parse .sav file into Pandas's data frame.

blocks|key|3474067|text|import+pandas+as+pd
import+pyreadstat+as+py

df,+meta+=+py.read_sav('file.SAV')
writer=pd.ExcelWriter+("file2.xlsx")
df.to_excel(writer,+'df')
df.to_csv('file2.csv')
writer.save()|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|3474068|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>import pandas as pd
import pyreadstat as py

df, meta = py.read_sav('file.SAV')
writer=pd.ExcelWriter ("file2.xlsx")
df.to_excel(writer, 'df')
df.to_csv('file2.csv')
writer.save()
</code></pre>

blocks|key|3714641|text|下面是我的类，它查找子文件夹中的所有.sav文件，并将它们转换为.csv格式。该类成功地处理了大小可达2GB的文件。如果只需要转换一个文件，只需使用convert_sav_to_csv(file_path,+dir_to_save=None)函数即可。我经常使用this+answer。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3714642|import+glob
import+pandas+as+pd
import+os
import+enum
import+errno
import+pyreadstat


#+Enum+for+size+units
class+SizeUnit(enum.Enum):
++++BYTES+=+1
++++KB+=+2
++++MB+=+3
++++GB+=+4


class+ConverterSavCsv:
++++@staticmethod
++++def+convert_unit(size_in_bytes,+unit):
++++++++"""+Convert+the+size+from+bytes+to+other+units+like+KB,+MB+or+GB"""
++++++++if+unit+==+SizeUnit.KB:
++++++++++++return+size_in_bytes+/+1024
++++++++elif+unit+==+SizeUnit.MB:
++++++++++++return+size_in_bytes+/+(1024+*+1024)
++++++++elif+unit+==+SizeUnit.GB:
++++++++++++return+size_in_bytes+/+(1024+*+1024+*+1024)
++++++++else:
++++++++++++return+size_in_bytes

++++@staticmethod
++++def+get_file_size(file_name,+size_type=SizeUnit.BYTES):
++++++++"""+Get+file+in+size+in+given+unit+like+KB,+MB+or+GB"""
++++++++size+=+os.path.getsize(file_name)
++++++++return+ConverterSavCsv.convert_unit(size,+size_type)

++++@staticmethod
++++def+find_all_sav_files(upper_dir_path):
++++++++if+not+os.path.isdir(dir_path):
++++++++++++raise+NotADirectoryError(dir_path)
++++++++files+=+glob.glob(upper_dir_path+%2B+'/**/*.sav',+recursive=True)
++++++++files_and_sizes+=+[]
++++++++for+elem+in+files:
++++++++++++elem_size+=+ConverterSavCsv.get_file_size(elem,+SizeUnit.MB)
++++++++++++files_and_sizes.append((elem,+elem_size))
++++++++result_df+=+pd.DataFrame(files_and_sizes,+columns=['Path',+'Size'])
++++++++result_df+=+result_df.sort_values(by=['Size'],+ascending=False)
++++++++result_df.to_excel(os.path.join(upper_dir_path,+r'spss_files_sizes.xlsx'),+index=False)
++++++++return+result_df

++++@staticmethod
++++def+_dir_filepath_prepare_before_conversion(file_path,+dir_to_save=None):
++++++++if+not+os.path.isfile(file_path):
++++++++++++raise+FileNotFoundError(errno.ENOENT,+os.strerror(errno.ENOENT),+file_path)
++++++++base_filename+=+os.path.basename(file_path)
++++++++base_filename+=+os.path.splitext(base_filename)[0]
++++++++if+dir_to_save+is+None:
++++++++++++current_dir+=+os.path.dirname(file_path)
++++++++++++dir_to_save+=+os.path.join(current_dir,+r'ready_csv')
++++++++++++os.makedirs(dir_to_save)
++++++++path_to_save+=+os.path.join(dir_to_save,+base_filename%2Br'.csv')
++++++++return+path_to_save

++++@staticmethod
++++def+convert_sav_to_csv(file_path,+dir_to_save=None):
++++++++path_to_save+=+ConverterSavCsv._dir_filepath_prepare_before_conversion(file_path,+dir_to_save)
++++++++_,+meta_start+=+pyreadstat.read_sav(file_path,+metadataonly=True)
++++++++number_of_rows+=+meta_start.number_rows
++++++++reader+=+pyreadstat.read_file_in_chunks(pyreadstat.read_sav,+file_path,+chunksize=1000)
++++++++cnt+=+0
++++++++for+df,+meta+in+reader:
++++++++++++if+number_of_rows+is+not+None:
++++++++++++++++print('Processing+rows',+cnt+*+1000,+'-',+(cnt+%2B+1)+*+1000,+'of',+number_of_rows)
++++++++++++if+cnt+>+0:
++++++++++++++++write_mode+=+"a"
++++++++++++++++header+=+False
++++++++++++else:
++++++++++++++++write_mode+=+"w"
++++++++++++++++header+=+True
++++++++++++df.to_csv(path_to_save,+mode=write_mode,+header=header)
++++++++++++cnt+%2B=+1
++++++++return+ConverterSavCsv.get_file_size(path_to_save,+SizeUnit.MB)

++++@staticmethod
++++def+convert_all_sav_files_to_csv(sav_files_df,+dir_to_save=None):
++++++++if+sav_files_df.empty+\
++++++++++++++++or+'Path'+not+in+sav_files_df.columns+\
++++++++++++++++or+'Size'+not+in+sav_files_df.columns:
++++++++++++raise+ValueError('Problems+with+input+Pandas+dataframe+in+convert_all_sav_files_to_csv+function')
++++++++if+dir_to_save+is+not+None:
++++++++++++if+not+os.path.isdir(dir_to_save):
++++++++++++++++os.makedirs(dir_to_save)
++++++++++++++++#+raise+NotADirectoryError(errno.ENOENT,+os.strerror(errno.ENOENT),+dir_to_save)
++++++++counter+=+1
++++++++files_total+=+sav_files_df.shape[0]
++++++++for+index,+row+in+sav_files_df.iterrows():
++++++++++++base_filename+=+os.path.basename(row['Path'])
++++++++++++base_filename+=+os.path.splitext(base_filename)[0]
++++++++++++print('Converting+file',+base_filename,+'-',+counter,+'of',+files_total,+',+with+size',+row['Size'],+'MB')
++++++++++++ConverterSavCsv.convert_sav_to_csv(row['Path'],+dir_to_save)
++++++++++++counter+=+counter+%2B+1
++++++++++++print()
++++++++return+files_total


if+__name__+==+"__main__":
++++dir_path+=+r'C:\Job\sav_files'
++++dir_to_save_path+=+r'C:\Job\sav_files\csv_ready_files'
++++#+if+dir_to_save_path+is+None,
++++#+it+creates+subdirectory+ready_csv+and+saves+there

++++#+find+all+SAV+files+in+the+directory+and+convert+them+to+CSV
++++converter+=+ConverterSavCsv()
++++df_sav_files_path_size+=+converter.find_all_sav_files(dir_path)
++++num_of_files+=+converter.convert_all_sav_files_to_csv(df_sav_files_path_size,+dir_to_save_path)
++++print(num_of_files,+'.sav+files+converted+to+.csv+and+saved')

++++#+for+single+file
++++#+use+function+convert_sav_to_csv(file_path,+dir_to_save=None)|code-block|syntax|javascript|3714643|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/63049155/3139228^0|I|4|W|4|22|1B|3N|B|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|13|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

Here is my class that finds all <code>.sav</code> files in subfolders and converts them to the <code>.csv</code> format. The class successfully coped with files up to 2 GB in size. If you only need to convert one file, just use the <code>convert_sav_to_csv(file_path, dir_to_save=None)</code> function. I used <a href="https://stackoverflow.com/a/63049155/3139228">this answer</a> a lot.
<pre><code>import glob
import pandas as pd
import os
import enum
import errno
import pyreadstat


# Enum for size units
class SizeUnit(enum.Enum):
 BYTES = 1
 KB = 2
 MB = 3
 GB = 4


class ConverterSavCsv:
 @staticmethod
 def convert_unit(size_in_bytes, unit):
 &quot;&quot;&quot; Convert the size from bytes to other units like KB, MB or GB&quot;&quot;&quot;
 if unit == SizeUnit.KB:
 return size_in_bytes / 1024
 elif unit == SizeUnit.MB:
 return size_in_bytes / (1024 * 1024)
 elif unit == SizeUnit.GB:
 return size_in_bytes / (1024 * 1024 * 1024)
 else:
 return size_in_bytes

 @staticmethod
 def get_file_size(file_name, size_type=SizeUnit.BYTES):
 &quot;&quot;&quot; Get file in size in given unit like KB, MB or GB&quot;&quot;&quot;
 size = os.path.getsize(file_name)
 return ConverterSavCsv.convert_unit(size, size_type)

 @staticmethod
 def find_all_sav_files(upper_dir_path):
 if not os.path.isdir(dir_path):
 raise NotADirectoryError(dir_path)
 files = glob.glob(upper_dir_path + '/**/*.sav', recursive=True)
 files_and_sizes = []
 for elem in files:
 elem_size = ConverterSavCsv.get_file_size(elem, SizeUnit.MB)
 files_and_sizes.append((elem, elem_size))
 result_df = pd.DataFrame(files_and_sizes, columns=['Path', 'Size'])
 result_df = result_df.sort_values(by=['Size'], ascending=False)
 result_df.to_excel(os.path.join(upper_dir_path, r'spss_files_sizes.xlsx'), index=False)
 return result_df

 @staticmethod
 def _dir_filepath_prepare_before_conversion(file_path, dir_to_save=None):
 if not os.path.isfile(file_path):
 raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), file_path)
 base_filename = os.path.basename(file_path)
 base_filename = os.path.splitext(base_filename)[0]
 if dir_to_save is None:
 current_dir = os.path.dirname(file_path)
 dir_to_save = os.path.join(current_dir, r'ready_csv')
 os.makedirs(dir_to_save)
 path_to_save = os.path.join(dir_to_save, base_filename+r'.csv')
 return path_to_save

 @staticmethod
 def convert_sav_to_csv(file_path, dir_to_save=None):
 path_to_save = ConverterSavCsv._dir_filepath_prepare_before_conversion(file_path, dir_to_save)
 _, meta_start = pyreadstat.read_sav(file_path, metadataonly=True)
 number_of_rows = meta_start.number_rows
 reader = pyreadstat.read_file_in_chunks(pyreadstat.read_sav, file_path, chunksize=1000)
 cnt = 0
 for df, meta in reader:
 if number_of_rows is not None:
 print('Processing rows', cnt * 1000, '-', (cnt + 1) * 1000, 'of', number_of_rows)
 if cnt &gt; 0:
 write_mode = &quot;a&quot;
 header = False
 else:
 write_mode = &quot;w&quot;
 header = True
 df.to_csv(path_to_save, mode=write_mode, header=header)
 cnt += 1
 return ConverterSavCsv.get_file_size(path_to_save, SizeUnit.MB)

 @staticmethod
 def convert_all_sav_files_to_csv(sav_files_df, dir_to_save=None):
 if sav_files_df.empty \
 or 'Path' not in sav_files_df.columns \
 or 'Size' not in sav_files_df.columns:
 raise ValueError('Problems with input Pandas dataframe in convert_all_sav_files_to_csv function')
 if dir_to_save is not None:
 if not os.path.isdir(dir_to_save):
 os.makedirs(dir_to_save)
 # raise NotADirectoryError(errno.ENOENT, os.strerror(errno.ENOENT), dir_to_save)
 counter = 1
 files_total = sav_files_df.shape[0]
 for index, row in sav_files_df.iterrows():
 base_filename = os.path.basename(row['Path'])
 base_filename = os.path.splitext(base_filename)[0]
 print('Converting file', base_filename, '-', counter, 'of', files_total, ', with size', row['Size'], 'MB')
 ConverterSavCsv.convert_sav_to_csv(row['Path'], dir_to_save)
 counter = counter + 1
 print()
 return files_total


if __name__ == &quot;__main__&quot;:
 dir_path = r'C:\Job\sav_files'
 dir_to_save_path = r'C:\Job\sav_files\csv_ready_files'
 # if dir_to_save_path is None,
 # it creates subdirectory ready_csv and saves there

 # find all SAV files in the directory and convert them to CSV
 converter = ConverterSavCsv()
 df_sav_files_path_size = converter.find_all_sav_files(dir_path)
 num_of_files = converter.convert_all_sav_files_to_csv(df_sav_files_path_size, dir_to_save_path)
 print(num_of_files, '.sav files converted to .csv and saved')

 # for single file
 # use function convert_sav_to_csv(file_path, dir_to_save=None)
</code></pre>

I tried to convert a SAV file into CSV by using the following code

<pre><code>data = pd.io.stata.read_stata("C:/Users/Nicola/Desktop/Relevant projects activities ACF/BRACED Final Evaluation/Evaluations/CSI_compil_2017.sav")
writer = pd.ExcelWriter('C:/Users/Nicola/Desktop/Baseline.xlsx')
data.to_excel(writer, 'data')
data.to_csv('changed_to_csv.csv')
writer.save()
</code></pre>

The output I am getting is the following

<blockquote>
 ValueError: Version of given Stata file is not 104, 105, 108, 111
 (Stata 7SE), 113 (Stata 8/9), 114 (Stata 10/11), 115 (Stata 12), 117
 (Stata 13), or 118 (Stata 14)
</blockquote>

Is there a better snippet I could use to perform this conversion more efficiently? Thanks

Convert SAV into CSV

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我试图使用以下代码将SAV文件转换为CSVdata = pd.io.stata.read_stata("C:/Users/Nicola/Desktop/Relevant projects activities ACF/BRACED Final Evaluation/Evaluations/CSI_compil_2017.sav")writer = pd.ExcelWriter('C:/Users/

问将SAV转换为CSV
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问将SAV转换为CSV
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