Cải thiện bài viết
Lưu bài viết
Cải thiện bài viết
Lưu bài viết
Đọc
Bàn luận
- Hãy cùng xem cách chuyển đổi một dữ liệu thành tệp CSV bằng cách sử dụng phân tách tab. Chúng tôi sẽ sử dụng phương thức TO_CSV [] để lưu DataFrame dưới dạng tệp CSV. Để lưu DataFrame với các dấu phân cách tab, chúng ta phải vượt qua \ \ t, làm tham số SEP trong phương thức TO_CSV [].
- Cách tiếp cận: & nbsp;
- Nhập các mô -đun gấu trúc và numpy.
- Tạo DataFrame bằng phương thức DataFrame [].
- Lưu DataFrame dưới dạng tệp CSV bằng phương thức TO_CSV [] với tham số SEP dưới dạng \ tiêu.
Python3
Tải tệp CSV mới được tạo bằng phương thức read_csv [] dưới dạng dataFrame.
Hiển thị DataFrame mới.
import
pandas as pd
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
7import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
8import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
4Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t10
Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t11
Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t12
Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t13
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
2Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t15
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
4Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t17
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
4Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t19
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
4>>> import collections >>> Q = collections.deque[] >>> Q.append[1] >>> Q.appendleft[2] >>> Q.extend[[3, 4]] >>> Q.extendleft[[5, 6]] >>> Q deque[[6, 5, 2, 1, 3, 4]] >>> Q.pop[] 4 >>> Q.popleft[] 6 >>> Q deque[[5, 2, 1, 3]] >>> Q.rotate[3] >>> Q deque[[2, 1, 3, 5]] >>> Q.rotate[-3] >>> Q deque[[5, 2, 1, 3]] >>> last_three = collections.deque[maxlen=3] >>> for i in range[4]: ... last_three.append[i] ... print ', '.join[str[x] for x in last_three] ... 0 0, 1 0, 1, 2 1, 2, 3 2, 3, 41
Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t11
Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t12
>>> import collections >>> Q = collections.deque[] >>> Q.append[1] >>> Q.appendleft[2] >>> Q.extend[[3, 4]] >>> Q.extendleft[[5, 6]] >>> Q deque[[6, 5, 2, 1, 3, 4]] >>> Q.pop[] 4 >>> Q.popleft[] 6 >>> Q deque[[5, 2, 1, 3]] >>> Q.rotate[3] >>> Q deque[[2, 1, 3, 5]] >>> Q.rotate[-3] >>> Q deque[[5, 2, 1, 3]] >>> last_three = collections.deque[maxlen=3] >>> for i in range[4]: ... last_three.append[i] ... print ', '.join[str[x] for x in last_three] ... 0 0, 1 0, 1, 2 1, 2, 3 2, 3, 44
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
2>>> import collections >>> Q = collections.deque[] >>> Q.append[1] >>> Q.appendleft[2] >>> Q.extend[[3, 4]] >>> Q.extendleft[[5, 6]] >>> Q deque[[6, 5, 2, 1, 3, 4]] >>> Q.pop[] 4 >>> Q.popleft[] 6 >>> Q deque[[5, 2, 1, 3]] >>> Q.rotate[3] >>> Q deque[[2, 1, 3, 5]] >>> Q.rotate[-3] >>> Q deque[[5, 2, 1, 3]] >>> last_three = collections.deque[maxlen=3] >>> for i in range[4]: ... last_three.append[i] ... print ', '.join[str[x] for x in last_three] ... 0 0, 1 0, 1, 2 1, 2, 3 2, 3, 46
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
4>>> import collections >>> Q = collections.deque[] >>> Q.append[1] >>> Q.appendleft[2] >>> Q.extend[[3, 4]] >>> Q.extendleft[[5, 6]] >>> Q deque[[6, 5, 2, 1, 3, 4]] >>> Q.pop[] 4 >>> Q.popleft[] 6 >>> Q deque[[5, 2, 1, 3]] >>> Q.rotate[3] >>> Q deque[[2, 1, 3, 5]] >>> Q.rotate[-3] >>> Q deque[[5, 2, 1, 3]] >>> last_three = collections.deque[maxlen=3] >>> for i in range[4]: ... last_three.append[i] ... print ', '.join[str[x] for x in last_three] ... 0 0, 1 0, 1, 2 1, 2, 3 2, 3, 48
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
4import
0import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
4import
2import
3import
numpy as np
students
=
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
0import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
1import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
2import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
3import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
4import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
5import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
6pandas as pd
5
>>> import collections >>> Q = collections.deque[] >>> Q.append[1] >>> Q.appendleft[2] >>> Q.extend[[3, 4]] >>> Q.extendleft[[5, 6]] >>> Q deque[[6, 5, 2, 1, 3, 4]] >>> Q.pop[] 4 >>> Q.popleft[] 6 >>> Q deque[[5, 2, 1, 3]] >>> Q.rotate[3] >>> Q deque[[2, 1, 3, 5]] >>> Q.rotate[-3] >>> Q deque[[5, 2, 1, 3]] >>> last_three = collections.deque[maxlen=3] >>> for i in range[4]: ... last_three.append[i] ... print ', '.join[str[x] for x in last_three] ... 0 0, 1 0, 1, 2 1, 2, 3 2, 3, 44
pandas as pd
7pandas as pd
8pandas as pd
9import
0import
1
pandas as pd
8import
3
import
4import
5import
6=
import
8import
1
import
4=
import
6
pandas as pd
8pandas as pd
9numpy as np
7import
1
pandas as pd
8students
0
Các
numpy as np
0=
numpy as np
2import
5import
1
Tôi phải định dạng lại dữ liệu của mình cho một phần mềm di truyền yêu cầu chia từng cột thành hai, ví dụ students
1. Tệp đầu ra được cho là phân loại tab. Tôi đang cố gắng làm điều đó trong gấu trúc:
import csv
import pandas as pd
import numpy as np
df = pd.DataFrame[np.random.randint[0,3, size = [10,5]],
columns=[ chr[c] for c in range[97, 97+5] ]]
def fake_alleles[x]:
if x==0:
return "A\tA"
if x==1:
return "A\tG"
if x==2:
return "G\tG"
plinkpast6 = df.applymap[fake_alleles]
plinkpast6.to_csv["test.ped", sep="\t", quoting=csv.QUOTE_NONE]
Điều đó cho tôi một lỗi students
2. Có những cách khác để làm điều đó với students
3 không?
Gấu trúc: Tập thể dục DataFrame-27 với giải pháp
Viết một chương trình gấu trúc để viết tệp dữ liệu vào tệp CSV bằng cách sử dụng phân tách tab.
Dữ liệu mẫu: DataFrame gốc Col1 Col2 Col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Dữ liệu từ New_File.CSV Tệp: COL1 \ TCOL2 \ TCOL3 0 1 \ T4 \ T7 1 4 \ T5 \ t8 2 3 \ t6 \ t9 3 4 \ t7 \ t0 4 5 \ t8 \ t1
Original DataFrame
col1 col2 col3
0 1 4 7
1 4 5 8
2 3 6 9
3 4 7 0
4 5 8 1
Data from new_file.csv file:
col1\tcol2\tcol3
0 1\t4\t7
1 4\t5\t8
2 3\t6\t9
3 4\t7\t0
4 5\t8\t1
Giải pháp mẫu::
Mã Python:
import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame[data=d]
print["Original DataFrame"]
print[df]
print['Data from new_file.csv file:']
df.to_csv['new_file.csv', sep='\t', index=False]
new_df = pd.read_csv['new_file.csv']
print[new_df]
Đầu ra mẫu:
Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t1
Trình chỉnh sửa mã Python-Pandas:
Có một cách khác để giải quyết giải pháp này? Đóng góp mã của bạn [và nhận xét] thông qua Disqus.
Trước đây: Viết một chương trình gấu trúc để thêm một hàng vào DataFrame hiện có. Write a Pandas program to add one row in an existing DataFrame.
Next: Write a Pandas program to count city wise number of people from a given of data set [city, name of the person].
Python: Lời khuyên trong ngày
Cấu trúc Deques [Deques là một khái quát của các ngăn xếp và hàng đợi]:
>>> import collections >>> Q = collections.deque[] >>> Q.append[1] >>> Q.appendleft[2] >>> Q.extend[[3, 4]] >>> Q.extendleft[[5, 6]] >>> Q deque[[6, 5, 2, 1, 3, 4]] >>> Q.pop[] 4 >>> Q.popleft[] 6 >>> Q deque[[5, 2, 1, 3]] >>> Q.rotate[3] >>> Q deque[[2, 1, 3, 5]] >>> Q.rotate[-3] >>> Q deque[[5, 2, 1, 3]] >>> last_three = collections.deque[maxlen=3] >>> for i in range[4]: ... last_three.append[i] ... print ', '.join[str[x] for x in last_three] ... 0 0, 1 0, 1, 2 1, 2, 3 2, 3, 4