Why does accessing columns of a pandas dataframe with .loc[] produce duplicate rows?
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
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
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
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
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
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
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
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
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
98.9k2160110
98.9k2160110
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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