Pandas ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series
Pandas ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series
参考Rounak Banik的书"Hands on Recommendation Systems with Python"，我正在尝试python中基于知识的推荐系统。我们有 IMDB 的电影数据集。
我在最终输出中遇到错误。请在下面查看我的整个代码，构建图表函数是我收到错误的地方。请帮我解决这个问题。谢谢你。
我无法在该平台上找到类似问题的答案。因此我发布了新问题。
import pandas as pd
import numpy as np
df = pd.read_csv('..../RecoSys/data/movies_metadata.csv')
#print all the features(or columns) of the dataFrame
df.columns
#only keep those that we require
df = df[['title', 'genres', 'release_date', 'runtime', 'vote_average', 'vote_count']]
df.head()
#convert release_date into pandas datetime format
df['release_date'] = pd.to_datetime(df['release_date'], errors='coerce')
df['year'] = df['release_date'].apply(lambda x: str(x).split('-')[0] if x!=np.nan else np.nan)
#Helper function to convert NaT to 0 and all other years to integers.
def convert_int(x):
try:
return int(x)
except:
return 0
#Apply convert_int to the year feature
df['year'] = df['year'].apply(convert_int)
#Drop the release_date column
df = df.drop('release_date', axis=1)
#Display the dataframe
df.head()
#Print genres of the first movie
df.iloc[0]['genres']
#Import the literal_eval function from ast
from ast import literal_eval
import json
#Define a stringified list and output its type
a = "[1,2,3]"
print(type(a))
#Apply literal_eval and output type
b = literal_eval(a)
print(type(b))
#Convert all NaN into stringified empty lists
df['genres'] = df['genres'].fillna('[]')
#Apply literal_eval to convert to the list object
df['genres'] = df['genres'].apply(literal_eval)
#df['genres'] = json.loads(df['genres'])
#Convert list of dictionaries to a list of strings
df['genres'] = df['genres'].apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])
df.head()
#Create a new feature by exploding genres
s = df.apply(lambda x: pd.Series(x['genres']),axis=1).stack().reset_index(level=1, drop=True)
#Name the new feature as 'genre'
s.name = 'genre'
#Create a new dataframe gen_df which by dropping the old 'genres' feature and adding the new 'genre'.
gen_df = df.drop('genres', axis=1).join(s)
#Print the head of the new gen_df
gen_df.head(15)
def build_chart(gen_df, percentile=0.8):
#Ask for preferred genres
print("Please Input preferred genre")
genre = input()
#Ask for lower limit of duration
print("Please Input shortest duration")
low_time = int(input())
#Ask for upper limit of duration
print("Please Input Longesr Duration")
high_time = int(input())
#Ask for lower limit of timeline
print("Input earliest year")
low_year = int(input())
#Ask for upper limit of timeline
print("Input latest year")
high_year = int(input())
#Define a new movies variable to store the preferred movies. Copy the contents of gen_df to movies
movies = gen_df.copy()
#Filter based on the condition
movies = movies[(movies['genre'] == genre) &
(movies['runtime'] >= low_time) &
(movies['runtime'] <= high_time) &
(movies['year'] >= low_year) &
(movies['year'] <= high_year)]
#Compute the values of C and m for the filtered movies
C = movies['vote_average'].mean()
m = movies['vote_count'].quantile(percentile)
#Only consider movies that have higher than m votes. Save this in a new dataframe q_movies
q_movies = movies.copy().loc[movies['vote_count'] >= m]
#Calculate score using the IMDB formula
q_movies['score'] = q_movies.apply(lambda x: (x['vote_count']/(x['vote_count']+m) * x['vote_average'])
+ (m/(m+x['vote_count']) * C), axis=1)
#Sort movies in descending order of their scores
q_movies = q_movies.sort_values('score', ascending=False)
return q_movies
build_chart(gen_df).head()
Please Input preferred genre
animation
Please Input shortest duration
30
Please Input Longesr Duration
120
Input earliest year
1990
Input latest year
2005
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _ensure_valid_index(self, value)
3423 try:
-> 3424 value = Series(value)
3425 except (ValueError, NotImplementedError, TypeError):
~\Anaconda3\lib\site-packages\pandas\core\series.py in __init__(self, data, index, dtype, name, copy, fastpath)
263
--> 264 data = SingleBlockManager(data, index, fastpath=True)
265
~\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in __init__(self, block, axis, do_integrity_check, fastpath)
1480 if not isinstance(block, Block):
-> 1481 block = make_block(block, placement=slice(0, len(axis)), ndim=1)
1482
~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in make_block(values, placement, klass, ndim, dtype, fastpath)
3094
-> 3095 return klass(values, ndim=ndim, placement=placement)
3096
~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in __init__(self, values, placement, ndim)
2630 super(ObjectBlock, self).__init__(values, ndim=ndim,
-> 2631 placement=placement)
2632
~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in __init__(self, values, placement, ndim)
86 'Wrong number of items passed {val}, placement implies '
---> 87 '{mgr}'.format(val=len(self.values), mgr=len(self.mgr_locs)))
88
ValueError: Wrong number of items passed 6, placement implies 0
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-53-3c3d1bc1cf24> in <module>
45 return q_movies
46
---> 47 build_chart(gen_df).head()
<ipython-input-53-3c3d1bc1cf24> in build_chart(gen_df, percentile)
39 #Calculate score using the IMDB formula
40 q_movies['score'] = q_movies.apply(lambda x: (x['vote_count']/(x['vote_count']+m) * x['vote_average'])
---> 41 + (m/(m+x['vote_count']) * C), axis=1)
42 #Sort movies in descending order of their scores
43 q_movies = q_movies.sort_values('score', ascending=False)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __setitem__(self, key, value)
3368 else:
3369 # set column
-> 3370 self._set_item(key, value)
3371
3372 def _setitem_slice(self, key, value):
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _set_item(self, key, value)
3442 """
3443
-> 3444 self._ensure_valid_index(value)
3445 value = self._sanitize_column(key, value)
3446 NDFrame._set_item(self, key, value)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _ensure_valid_index(self, value)
3424 value = Series(value)
3425 except (ValueError, NotImplementedError, TypeError):
-> 3426 raise ValueError('Cannot set a frame with no defined index '
3427 'and a value that cannot be converted to a '
3428 'Series')
ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series
It seems I have entered wrong inputs, hence the reason I had this
error. Below is the snapshot of the code results.
These are case sensitive inputs. That was the reason why I had this
issue.
参考Rounak Banik的书"Hands on Recommendation Systems with Python"，我正在尝试python中基于知识的推荐系统。我们有 IMDB 的电影数据集。
我在最终输出中遇到错误。请在下面查看我的整个代码，构建图表函数是我收到错误的地方。请帮我解决这个问题。谢谢你。
我无法在该平台上找到类似问题的答案。因此我发布了新问题。
import pandas as pd
import numpy as np
df = pd.read_csv('..../RecoSys/data/movies_metadata.csv')
#print all the features(or columns) of the dataFrame
df.columns
#only keep those that we require
df = df[['title', 'genres', 'release_date', 'runtime', 'vote_average', 'vote_count']]
df.head()
#convert release_date into pandas datetime format
df['release_date'] = pd.to_datetime(df['release_date'], errors='coerce')
df['year'] = df['release_date'].apply(lambda x: str(x).split('-')[0] if x!=np.nan else np.nan)
#Helper function to convert NaT to 0 and all other years to integers.
def convert_int(x):
try:
return int(x)
except:
return 0
#Apply convert_int to the year feature
df['year'] = df['year'].apply(convert_int)
#Drop the release_date column
df = df.drop('release_date', axis=1)
#Display the dataframe
df.head()
#Print genres of the first movie
df.iloc[0]['genres']
#Import the literal_eval function from ast
from ast import literal_eval
import json
#Define a stringified list and output its type
a = "[1,2,3]"
print(type(a))
#Apply literal_eval and output type
b = literal_eval(a)
print(type(b))
#Convert all NaN into stringified empty lists
df['genres'] = df['genres'].fillna('[]')
#Apply literal_eval to convert to the list object
df['genres'] = df['genres'].apply(literal_eval)
#df['genres'] = json.loads(df['genres'])
#Convert list of dictionaries to a list of strings
df['genres'] = df['genres'].apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])
df.head()
#Create a new feature by exploding genres
s = df.apply(lambda x: pd.Series(x['genres']),axis=1).stack().reset_index(level=1, drop=True)
#Name the new feature as 'genre'
s.name = 'genre'
#Create a new dataframe gen_df which by dropping the old 'genres' feature and adding the new 'genre'.
gen_df = df.drop('genres', axis=1).join(s)
#Print the head of the new gen_df
gen_df.head(15)
def build_chart(gen_df, percentile=0.8):
#Ask for preferred genres
print("Please Input preferred genre")
genre = input()
#Ask for lower limit of duration
print("Please Input shortest duration")
low_time = int(input())
#Ask for upper limit of duration
print("Please Input Longesr Duration")
high_time = int(input())
#Ask for lower limit of timeline
print("Input earliest year")
low_year = int(input())
#Ask for upper limit of timeline
print("Input latest year")
high_year = int(input())
#Define a new movies variable to store the preferred movies. Copy the contents of gen_df to movies
movies = gen_df.copy()
#Filter based on the condition
movies = movies[(movies['genre'] == genre) &
(movies['runtime'] >= low_time) &
(movies['runtime'] <= high_time) &
(movies['year'] >= low_year) &
(movies['year'] <= high_year)]
#Compute the values of C and m for the filtered movies
C = movies['vote_average'].mean()
m = movies['vote_count'].quantile(percentile)
#Only consider movies that have higher than m votes. Save this in a new dataframe q_movies
q_movies = movies.copy().loc[movies['vote_count'] >= m]
#Calculate score using the IMDB formula
q_movies['score'] = q_movies.apply(lambda x: (x['vote_count']/(x['vote_count']+m) * x['vote_average'])
+ (m/(m+x['vote_count']) * C), axis=1)
#Sort movies in descending order of their scores
q_movies = q_movies.sort_values('score', ascending=False)
return q_movies
build_chart(gen_df).head()
Please Input preferred genre
animation
Please Input shortest duration
30
Please Input Longesr Duration
120
Input earliest year
1990
Input latest year
2005
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _ensure_valid_index(self, value)
3423 try:
-> 3424 value = Series(value)
3425 except (ValueError, NotImplementedError, TypeError):
~\Anaconda3\lib\site-packages\pandas\core\series.py in __init__(self, data, index, dtype, name, copy, fastpath)
263
--> 264 data = SingleBlockManager(data, index, fastpath=True)
265
~\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in __init__(self, block, axis, do_integrity_check, fastpath)
1480 if not isinstance(block, Block):
-> 1481 block = make_block(block, placement=slice(0, len(axis)), ndim=1)
1482
~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in make_block(values, placement, klass, ndim, dtype, fastpath)
3094
-> 3095 return klass(values, ndim=ndim, placement=placement)
3096
~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in __init__(self, values, placement, ndim)
2630 super(ObjectBlock, self).__init__(values, ndim=ndim,
-> 2631 placement=placement)
2632
~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in __init__(self, values, placement, ndim)
86 'Wrong number of items passed {val}, placement implies '
---> 87 '{mgr}'.format(val=len(self.values), mgr=len(self.mgr_locs)))
88
ValueError: Wrong number of items passed 6, placement implies 0
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-53-3c3d1bc1cf24> in <module>
45 return q_movies
46
---> 47 build_chart(gen_df).head()
<ipython-input-53-3c3d1bc1cf24> in build_chart(gen_df, percentile)
39 #Calculate score using the IMDB formula
40 q_movies['score'] = q_movies.apply(lambda x: (x['vote_count']/(x['vote_count']+m) * x['vote_average'])
---> 41 + (m/(m+x['vote_count']) * C), axis=1)
42 #Sort movies in descending order of their scores
43 q_movies = q_movies.sort_values('score', ascending=False)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __setitem__(self, key, value)
3368 else:
3369 # set column
-> 3370 self._set_item(key, value)
3371
3372 def _setitem_slice(self, key, value):
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _set_item(self, key, value)
3442 """
3443
-> 3444 self._ensure_valid_index(value)
3445 value = self._sanitize_column(key, value)
3446 NDFrame._set_item(self, key, value)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _ensure_valid_index(self, value)
3424 value = Series(value)
3425 except (ValueError, NotImplementedError, TypeError):
-> 3426 raise ValueError('Cannot set a frame with no defined index '
3427 'and a value that cannot be converted to a '
3428 'Series')
ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series
It seems I have entered wrong inputs, hence the reason I had this error. Below is the snapshot of the code results.
These are case sensitive inputs. That was the reason why I had this issue.