I have 12 months sales data for each month. I want to analyze the dataset as a whole.
I have tried using the concat function but It produces not a number (NaN) in my dataframe fields.
In R, cbind function solves this. How do i approach this differently in Python?
I tried using df.concat function to bind the rows cos all the column names are the same for the datasets.
What other options can i explore?
sales_1 = pd.read_csv('Sales_January_2019.csv')
sales_2 = pd.read_csv('Sales_February_2019.csv')
sales_3 = pd.read_csv('Sales_March_2019.csv')
sales_4 = pd.read_csv('Sales_April_2019.csv')
sales_5 = pd.read_csv('Sales_May_2019.csv')
sales_6 = pd.read_csv('Sales_June_2019.csv')
sales_7 = pd.read_csv('Sales_July_2019.csv')
sales_8 = pd.read_csv('Sales_August_2019.csv')
sales_9 = pd.read_csv('Sales_September_2019.csv')
sales_10 = pd.read_csv('Sales_October_2019.csv')
sales_11 = pd.read_csv('Sales_November_2019.csv')
sales_12 = pd.read_csv('Sales_December_2019.csv')
I expect all data frame to be merged into one since the column names are the same for all
perhaps
# using concat with the list of the DF that you already read-in to combine into a single DF
pd.concat([sales_1 ,sales_2 ,sales_3 ,sales_4 ,sales_5 ,sales_6 ,sales_7 ,sales_8 ,sales_9 ,sales_10 ,sales_11 ,sales_12 ])
Related
I have a list of 16 dataframes that contain stats for each player in the NBA during the respective season. My end goal is to run unsupervised learning algorithms on the data frames. For example, I want to see if I can determine a player's position by their stats or if I can determine their total points during the season based on their stats.
What I would like to do is modify the list(df_list), unless there's a better solution, of these dataframes instead modifying each dataframe to:
Change the datatype of the MP(minutes played column from str to int.
Modify the dataframe where there are only players with 1000 or more MP and there are no duplicate players(Rk)
(for instance in a season, a player(Rk) can play for three teams in a season and have 200MP, 300MP, and 400MP mins with each team. He'll have a column for each team and a column called TOT which will render his MP as 900(200+300+400) for a total of four rows in the dataframe. I only need the TOT row
Use simple algebra with various and individual columns columns, for example: being able to total the MP column and the PTS column and then diving the sum of the PTS column by the MP column.
Or dividing the total of the PTS column by the len of the PTS column.
What I've done so far is this:
Import my libraries and create 16 dataframes using pd.read_html(url).
The first dataframes created using two lines of code:
url = "https://www.basketball-reference.com/leagues/NBA_1997_totals.html"
ninetysix = pd.read_html(url)[0]
HOWEVER, the next four data frames had to be created using a few additional line of code(I received an error code that said "html5lib not found, please install it" so I downloaded both html5lib and requests). I say that to say...this distinction in creating the DF may have to considered in a solution.
The code I used:
import requests
import uuid
url = 'https://www.basketball-reference.com/leagues/NBA_1998_totals.html'
cookies = {'euConsentId': str(uuid.uuid4())}
html = requests.get(url, cookies=cookies).content
ninetyseven = pd.read_html(html)[0]
These four data frames look like this:
I tried this but it didn't do anything:
df_list = [
eightyfour, eightyfive, eightysix, eightyseven,
eightyeight, eightynine, ninety, ninetyone,
ninetytwo, ninetyfour, ninetyfive,
ninetysix, ninetyseven, ninetyeight, owe_one, owe_two
]
for df in df_list:
df = df.loc[df['Tm'] == 'TOT']
df = df.copy()
df['MP'] = df['MP'].astype(int)
df['Rk'] = df['Rk'].astype(int)
df = list(df[df['MP'] >= 1000]['Rk'])
df = df[df['Rk'].isin(df)]
owe_two
============================UPDATE===================================
This code will solves a portion of problem # 2
url = 'https://www.basketball-reference.com/leagues/NBA_1997_totals.html'
dd = pd.read_html(url)[0]
dd = dd[dd['Rk'].ne('Rk')]
dd['MP'] = dd['MP'].astype(int)
players_1000_rk_list = list(dd[dd['MP'] >= 1000]['Rk'])
players_dd = dd[dd['Rk'].isin(players_1000_rk_list)]
But it doesn't remove the duplicates.
==================== UPDATE 10/11/22 ================================
Let's say I take rows with values "TOT" in the "Tm" and create a new DF with them, and these rows from the original data frame...
could I then compare the new DF with the original data frame and remove the names from the original data IF they match the names from the new data frame?
the problem is that the df you are working on in the loop is not the same df that is in the df_list. you could solve this by saving the new df back to the list, overwriting the old df
for i,df in enumerate(df_list):
df = df.loc[df['Tm'] == 'TOT']
df = df.copy()
df['MP'] = df['MP'].astype(int)
df['Rk'] = df['Rk'].astype(int)
df = list(df[df['MP'] >= 1000]['Rk'])
df = df[df['Rk'].isin(df)]
df_list[i] = df
the2 lines are probably wrong as well
df = list(df[df['MP'] >= 1000]['Rk'])
df = df[df['Rk'].isin(df)]
perhaps you want this
for i,df in enumerate(df_list):
df = df.loc[df['Tm'] == 'TOT']
df = df.copy()
df['MP'] = df['MP'].astype(int)
df['Rk'] = df['Rk'].astype(int)
#df = list(df[df['MP'] >= 1000]['Rk'])
#df = df[df['Rk'].isin(df)]
# just the rows where MP > 1000
df_list[i] = df[df['MP'] >= 1000]
Let us say I have the following columns in a data frame:
title
year
actor1
actor2
cast_count
actor1_fb_likes
actor2_fb_likes
movie_fb_likes
I want to select the following columns from the data frame and ignore the rest of the columns :
the first 2 columns (title and year)
some columns based on name - cast_count
some columns which contain the string "actor1" - actor1 and actor1_fb_likes
I am new to pandas. For each of the above operations, I know what method to use. But I want to do all three operations together as all I want is a dataframe that contains the above columns that I need for further analysis. How do I do this?
Here is example code that I have written:
data = {
"title":['Hamlet','Avatar','Spectre'],
"year":['1979','1985','2007'],
"actor1":['Christoph Waltz','Tom Hardy','Doug Walker'],
"actor2":['Rob Walker','Christian Bale ','Tom Hardy'],
"cast_count":['15','24','37'],
"actor1_fb_likes":[545,782,100],
"actor2_fb_likes":[50,78,35],
"movie_fb_likes":[1200,750,475],
}
df_input = pd.DataFrame(data)
print(df_input)
df1 = df_input.iloc[:,0:2] # Select first 2 columns
df2 = df_input[['cast_count']] #select some columns by name - cast_count
df3 = df_input.filter(like='actor1') #select columns which contain the string "actor1" - actor1 and actor1_fb_likes
df_output = pd.concat(df1,df2, df3) #This throws an error that i can't understand the reason
print(df_output)
Question 1:
df_1 = df[['title', 'year']]
Question 2:
# This is an example but you can put whatever criteria you'd like
df_2 = df[df['cast_count'] > 10]
Question 3:
# This is an example but you can put whatever criteria you'd like this way
df_2 = df[(df['actor1_fb_likes'] > 1000) & (df['actor1'] == 'actor1')]
Make sure each filter is contained within it's own set of parenthesis () before using the & or | operators. & acts as an and operator. | acts as an or operator.
I'm trying to understand how to concat three individual dataframes (i.e df1, df2, df3) into a new dataframe say df4 whereby each individual dataframe has its own column left to right order.
I've tried using concat with axis = 1 to do this, but it appears not possible to automate this with a single action.
Table1_updated = pd.DataFrame(columns=['3P','2PG-3Io','3Io'])
Table1_updated=pd.concat([get_table1_3P,get_table1_2P_max_3Io,get_table1_3Io])
Note that with the exception of get_table1_2P_max_3Io, which has two columns, all other dataframes have one column
For example,
get_table1_3P =
get_table1_2P_max_3Io =
get_table1_3Io =
Ultimately, i would like to see the following:
I believe you need first concat and tthen change order by list of columns names:
Table1_updated=pd.concat([get_table1_3P,get_table1_2P_max_3Io,get_table1_3Io], axis=1)
Table1_updated = Table1_updated[['3P','2PG-3Io','3Io']]
I tried to perform my self-created function on a for loop, but it does not work as expected.
Some remarks in advance:
ma_strategy is my function and requires three inputs
ticker_list is a list with strings
result is a pandas Dataframe with 7 columns and I can call the column 'return_cum' with result['return_cum']. The rows of this column are containing floating point numbers.
These for loops doesn't work:
for i in ticker_list:
result = ma_strategy(i, 20, 5)
x = result['return_cum']
sample_returns = pd.DataFrame
y = pd.merge(x.to_frame(),sample_returns, left_index=True)
for i in ticker_list:
result = ma_strategy(i, 20, 5)
x = result[['return_cum']]
sample_returns = pd.DataFrame
y = pd.concat([sample_returns, x], axis=1)
My intention is the following:
The for loop should iterate over the items in my ticker_list and should save the 'return_cum' columns in x. Then the 'return_cum' columns should be stored in y together so that at the end I get a DataFrame with all the 'return_cum' columns of my ticker list.
How can I achieve that goal? I tried pd.concoat and merge, but nothing works.
Thanks for your help!
I have two dataframes, one contains screen names/display names and another contains individuals, and I am trying to create a third dataframe that contains all the data from each dataframe in a new row for each time a last name appears in the screen name/display name. Functionally this will create a list of possible matching names. My current code, which works perfectly but very slowly, looks like this:
# Original Social Media Screen Names
# cols = 'userid','screen_name','real_name'
usernames = pd.read_csv('social_media_accounts.csv')
# List Of Individuals To Match To Accounts
# cols = 'first_name','last_name'
individuals = pd.read_csv('individuals_list.csv')
userid, screen_name, real_name, last_name, first_name = [],[],[],[],[]
for index1, row1 in individuals.iterrows():
for index2, row2 in usernames.iterrows():
if (row2['Screen_Name'].lower().find(row1['Last_Name'].lower()) != -1) | (row2['Real_Name'].lower().find(row1['Last_Name'].lower()) != -1):
userid.append(row2['UserID'])
screen_name.append(row2['Screen_Name'])
real_name.append(row2['Real_Name'])
last_name.append(row1['Last_Name'])
first_name.append(row1['First_Name'])
cols = ['UserID', 'Screen_Name', 'Real_Name', 'Last_Name', 'First_Name']
index = range(0, len(userid))
match_list = pd.DataFrame(index=index, columns=cols)
match_list = match_list.fillna('')
match_list['UserID'] = userid
match_list['Screen_Name'] = screen_name
match_list['Real_Name'] = real_name
match_list['Last_Name'] = last_name
match_list['First_Name'] = first_name
Because I need the whole row from each column, the list comprehension methods I have tried do not seem to work.
The thing you want is to iterate through a dataframe faster. Doing that with a list comprehension is, taking data out of a pandas dataframe, handling it using operations in python, then putting it back in a pandas dataframe. The fastest way (currently, with small data) would be to handle it using pandas iteration methods.
The next thing you want to do is work with 2 dataframes. There is a tool in pandas called join.
result = pd.merge(usernames, individuals, on=['Screen_Name', 'Last_Name'])
After the merge you can do your filtering.
Here is the documentation: http://pandas.pydata.org/pandas-docs/stable/merging.html