Why does accessing columns of a pandas dataframe with .loc[] produce duplicate rows?
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Why is .loc producing duplicate rows in my DataFrame? I'm trying to select a few columns from m3, a DataFrame with 47 columns,to create a new DataFrame called output.
The problem: after accessing m3's columns with .loc, output has way more duplicates than m3 started with. Where could these duplicates have come from? I haven't found anything online about .loc duplicating rows. The output DataFrame is declared on the line that reads output = m3.loc[...], by the way.
The Code:
print("ARE THERE DUPLICATES in m3? ")
print(m3.duplicated().loc[lambda x: x==True])
output = m3.loc[:,["PLC_name", "line", "track", "notes", "final_source",
"s_name", "s_line", "s_track", "loc", "alt_loc", "suffix", "alt_match_name"]]
print("ARE THERE DUPLICATES in output? ")
print(output.duplicated().loc[lambda x: x==True].size, "duplicates")
The Terminal Output:
ARE THERE DUPLICATES in m3?
5241 True
5242 True
5243 True
5355 True
5356 True
5357 True
dtype: bool
ARE THERE DUPLICATES in output?
1838 duplicates
Of course, I could easily fix the problem by calling .drop_duplicates(keep="first"), but I'm more interesting in learning why .loc displays this behavior.
python pandas csv dataframe duplicates
asked Nov 16 '18 at 22:57
DavidDavid
316
add a comment |
Why is .loc producing duplicate rows in my DataFrame? I'm trying to select a few columns from m3, a DataFrame with 47 columns,to create a new DataFrame called output.
The problem: after accessing m3's columns with .loc, output has way more duplicates than m3 started with. Where could these duplicates have come from? I haven't found anything online about .loc duplicating rows. The output DataFrame is declared on the line that reads output = m3.loc[...], by the way.
The Code:
print("ARE THERE DUPLICATES in m3? ")
print(m3.duplicated().loc[lambda x: x==True])
output = m3.loc[:,["PLC_name", "line", "track", "notes", "final_source",
"s_name", "s_line", "s_track", "loc", "alt_loc", "suffix", "alt_match_name"]]
print("ARE THERE DUPLICATES in output? ")
print(output.duplicated().loc[lambda x: x==True].size, "duplicates")
The Terminal Output:
ARE THERE DUPLICATES in m3?
5241 True
5242 True
5243 True
5355 True
5356 True
5357 True
dtype: bool
ARE THERE DUPLICATES in output?
1838 duplicates
Of course, I could easily fix the problem by calling .drop_duplicates(keep="first"), but I'm more interesting in learning why .loc displays this behavior.
python pandas csv dataframe duplicates
asked Nov 16 '18 at 22:57
DavidDavid
316
add a comment |
Why is .loc producing duplicate rows in my DataFrame? I'm trying to select a few columns from m3, a DataFrame with 47 columns,to create a new DataFrame called output.
The problem: after accessing m3's columns with .loc, output has way more duplicates than m3 started with. Where could these duplicates have come from? I haven't found anything online about .loc duplicating rows. The output DataFrame is declared on the line that reads output = m3.loc[...], by the way.
The Code:
print("ARE THERE DUPLICATES in m3? ")
print(m3.duplicated().loc[lambda x: x==True])
output = m3.loc[:,["PLC_name", "line", "track", "notes", "final_source",
"s_name", "s_line", "s_track", "loc", "alt_loc", "suffix", "alt_match_name"]]
print("ARE THERE DUPLICATES in output? ")
print(output.duplicated().loc[lambda x: x==True].size, "duplicates")
The Terminal Output:
ARE THERE DUPLICATES in m3?
5241 True
5242 True
5243 True
5355 True
5356 True
5357 True
dtype: bool
ARE THERE DUPLICATES in output?
1838 duplicates
Of course, I could easily fix the problem by calling .drop_duplicates(keep="first"), but I'm more interesting in learning why .loc displays this behavior.
python pandas csv dataframe duplicates
asked Nov 16 '18 at 22:57
DavidDavid
316
Why is .loc producing duplicate rows in my DataFrame? I'm trying to select a few columns from m3, a DataFrame with 47 columns,to create a new DataFrame called output.
The problem: after accessing m3's columns with .loc, output has way more duplicates than m3 started with. Where could these duplicates have come from? I haven't found anything online about .loc duplicating rows. The output DataFrame is declared on the line that reads output = m3.loc[...], by the way.
The Code:
print("ARE THERE DUPLICATES in m3? ")
print(m3.duplicated().loc[lambda x: x==True])
output = m3.loc[:,["PLC_name", "line", "track", "notes", "final_source",
"s_name", "s_line", "s_track", "loc", "alt_loc", "suffix", "alt_match_name"]]
print("ARE THERE DUPLICATES in output? ")
print(output.duplicated().loc[lambda x: x==True].size, "duplicates")
The Terminal Output:
ARE THERE DUPLICATES in m3?
5241 True
5242 True
5243 True
5355 True
5356 True
5357 True
dtype: bool
ARE THERE DUPLICATES in output?
1838 duplicates
Of course, I could easily fix the problem by calling .drop_duplicates(keep="first"), but I'm more interesting in learning why .loc displays this behavior.
python pandas csv dataframe duplicates
python pandas csv dataframe duplicates
asked Nov 16 '18 at 22:57
DavidDavid
316
asked Nov 16 '18 at 22:57
DavidDavid
316
asked Nov 16 '18 at 22:57
DavidDavid
316
asked Nov 16 '18 at 22:57
DavidDavid
316
asked Nov 16 '18 at 22:57
DavidDavid
316
316
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
output filters for selected columns from m3. When you call duplicated on m3, all columns from the original dataframe are considered. When you call duplicated on output, only a subset of those columns is considered.
Therefore, you can have duplicates in output even when there are no duplicates in m3.
Here's a minimal and reproducible example of what you're seeing:
df = pd.DataFrame([[3, 8, 9], [4, 8, 9]])
print(df.duplicated().sum(), 'duplicates')
# 0 duplicates
df_filtered = df.loc[:, [1, 2]]
print(df_filtered.duplicated().sum(), 'duplicates')
# 1 duplicates
answered Nov 16 '18 at 23:01
jppjpp
103k2167117
1
Thanks @jpp! I was looking at this for a solid hour and now I'm having a real "duh" moment. Like why didn't I see it sooner! Anyhow, I upvoted your answer too, I suppose it'll show when I have more reputation.
– David
Nov 16 '18 at 23:23
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
output filters for selected columns from m3. When you call duplicated on m3, all columns from the original dataframe are considered. When you call duplicated on output, only a subset of those columns is considered.
Therefore, you can have duplicates in output even when there are no duplicates in m3.
Here's a minimal and reproducible example of what you're seeing:
df = pd.DataFrame([[3, 8, 9], [4, 8, 9]])
print(df.duplicated().sum(), 'duplicates')
# 0 duplicates
df_filtered = df.loc[:, [1, 2]]
print(df_filtered.duplicated().sum(), 'duplicates')
# 1 duplicates
answered Nov 16 '18 at 23:01
jppjpp
103k2167117
1
Thanks @jpp! I was looking at this for a solid hour and now I'm having a real "duh" moment. Like why didn't I see it sooner! Anyhow, I upvoted your answer too, I suppose it'll show when I have more reputation.
– David
Nov 16 '18 at 23:23
add a comment |
output filters for selected columns from m3. When you call duplicated on m3, all columns from the original dataframe are considered. When you call duplicated on output, only a subset of those columns is considered.
Therefore, you can have duplicates in output even when there are no duplicates in m3.
Here's a minimal and reproducible example of what you're seeing:
df = pd.DataFrame([[3, 8, 9], [4, 8, 9]])
print(df.duplicated().sum(), 'duplicates')
# 0 duplicates
df_filtered = df.loc[:, [1, 2]]
print(df_filtered.duplicated().sum(), 'duplicates')
# 1 duplicates
answered Nov 16 '18 at 23:01
jppjpp
103k2167117
1
Thanks @jpp! I was looking at this for a solid hour and now I'm having a real "duh" moment. Like why didn't I see it sooner! Anyhow, I upvoted your answer too, I suppose it'll show when I have more reputation.
– David
Nov 16 '18 at 23:23
add a comment |
output filters for selected columns from m3. When you call duplicated on m3, all columns from the original dataframe are considered. When you call duplicated on output, only a subset of those columns is considered.
Therefore, you can have duplicates in output even when there are no duplicates in m3.
Here's a minimal and reproducible example of what you're seeing:
df = pd.DataFrame([[3, 8, 9], [4, 8, 9]])
print(df.duplicated().sum(), 'duplicates')
# 0 duplicates
df_filtered = df.loc[:, [1, 2]]
print(df_filtered.duplicated().sum(), 'duplicates')
# 1 duplicates
answered Nov 16 '18 at 23:01
jppjpp
103k2167117
output filters for selected columns from m3. When you call duplicated on m3, all columns from the original dataframe are considered. When you call duplicated on output, only a subset of those columns is considered.
Therefore, you can have duplicates in output even when there are no duplicates in m3.
Here's a minimal and reproducible example of what you're seeing:
df = pd.DataFrame([[3, 8, 9], [4, 8, 9]])
print(df.duplicated().sum(), 'duplicates')
# 0 duplicates
df_filtered = df.loc[:, [1, 2]]
print(df_filtered.duplicated().sum(), 'duplicates')
# 1 duplicates
answered Nov 16 '18 at 23:01
jppjpp
103k2167117
answered Nov 16 '18 at 23:01
jppjpp
103k2167117
answered Nov 16 '18 at 23:01
jppjpp
103k2167117
answered Nov 16 '18 at 23:01
jppjpp
103k2167117
103k2167117
1
Thanks @jpp! I was looking at this for a solid hour and now I'm having a real "duh" moment. Like why didn't I see it sooner! Anyhow, I upvoted your answer too, I suppose it'll show when I have more reputation.
– David
Nov 16 '18 at 23:23
add a comment |
1
Thanks @jpp! I was looking at this for a solid hour and now I'm having a real "duh" moment. Like why didn't I see it sooner! Anyhow, I upvoted your answer too, I suppose it'll show when I have more reputation.
– David
Nov 16 '18 at 23:23
1
1
Thanks @jpp! I was looking at this for a solid hour and now I'm having a real "duh" moment. Like why didn't I see it sooner! Anyhow, I upvoted your answer too, I suppose it'll show when I have more reputation.
– David
Nov 16 '18 at 23:23
Thanks @jpp! I was looking at this for a solid hour and now I'm having a real "duh" moment. Like why didn't I see it sooner! Anyhow, I upvoted your answer too, I suppose it'll show when I have more reputation.
– David
Nov 16 '18 at 23:23
add a comment |
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