Python 3: Creating DataFrame with parsed data - pandas
The following data has been parsed from a stock API. The dataframe has the headers of each column in the Dataset respectively. Is there anyway I can link the data to the dataframe effectively creating a labeled data array/table?
DataFrame
df = pd.DataFrame(columns=['Date','Close','High','Low','Open','Volume'])
DataSet
20140502,36.8700,37.1200,36.2100,36.5900,22454100
20140505,36.9100,37.0500,36.3000,36.6800,13129100
20140506,36.4900,37.1700,36.4800,36.9400,19156000
20140507,34.0700,35.9900,33.6700,35.9900,66062700
20140508,33.9200,34.5700,33.6100,33.8800,30407700
20140509,33.7600,34.1000,33.4100,34.0100,20303400
20140512,34.4500,34.6000,33.8700,33.9900,22520600
20140513,34.4000,34.6900,34.1700,34.4300,12477100
20140514,34.1700,34.6500,33.9800,34.4800,17039000
20140515,33.8000,34.1900,33.4000,34.1800,18879800
20140516,33.4100,33.6600,33.1000,33.6600,18847100
20140519,33.8900,33.9900,33.2800,33.4100,14845700
20140520,33.8700,34.4700,33.6700,33.9900,18596700
20140521,34.3600,34.3900,33.8900,34.0000,13804500
20140522,34.7000,34.8600,34.2600,34.6000,17522800
20140523,35.0200,35.0800,34.5100,34.8500,16294400
20140527,35.1200,35.1300,34.7300,35.0000,13057000
20140528,34.7800,35.1700,34.4200,35.1500,16960500
20140529,34.9000,35.1000,34.6700,34.9000,9780800
20140530,34.6500,34.9300,34.1300,34.9200,13153000
20140602,34.8700,34.9500,34.2800,34.6900,9178900
20140603,34.6500,34.9700,34.5800,34.8000,6557500
20140604,34.7300,34.8300,34.2600,34.4800,9434100
I'm assuming that you are receiving the data as a list of lists. So something like -
vals = [[20140502,36.8700,37.1200,36.2100,36.5900,22454100], [20140505,36.9100,37.0500,36.3000,36.6800,13129100], ...]
In that case, you can populate your dataframe with loc -
for index, val in enumerate(vals):
df.loc[index] = val
Which will give you -
In [6]: df
Out[6]:
Date Close High Low Open Volume
0 20140502 36.87 37.12 36.21 36.59 22454100
1 20140505 36.91 37.05 36.3 36.68 13129100
...
Here, enumerate gives us the index of the row, so we can use that to populate the dataframe index.
If somehow the data was saved as csv, then you can simply use read_csv -
df = pd.read_csv('data.csv', names=['Date','Close','High','Low','Open','Volume'])
Related
Why would an extra column (unnamed: 0) appear after saving the df and then reading it through pd.read_csv?
My code to save the df is:
fdi_out_vdem.to_csv("fdi_out_vdem.csv")
To read the df into python is :
fdi_out_vdem = pd.read_csv("C:/Users/asus/Desktop/classen/fdi_out_vdem.csv")
The df:
Unnamed: 0
country_name
value
1
Spain
190
2
Spain
311
Your df has two columns, but also an index with "0" and "1". When writing it to csv it looks like this:
,country_name,value
0,Spain,190
1,Spain,311
When importing it with pandas you it is considered as df with 3 columns (and the first has no name)
You have two possibilities here:
Save it without index column:
df.to_csv("fdi_out_vdem.csv", index=False)
df = pd.read_csv("C:/Users/asus/Desktop/classen/fdi_out_vdem.csv")
or save it with index column and define an index col when reading it with pd.read_csv
df.to_csv("fdi_out_vdem.csv")
df = pd.read_csv("C:/Users/asus/Desktop/classen/fdi_out_vdem.csv", index_col=[0])
UPDATE
As recommended by #ouroboros1 in the comments you could also name your index before saving it to csv, so you can define the index column by using that name
df.index.name = "index"
df.to_csv("fdi_out_vdem.csv")
df = pd.read_csv("C:/Users/asus/Desktop/classen/fdi_out_vdem.csv", index_col="index")
You can either pass the parameter index_col=[0] to pandas.read_csv :
fdi_out_vdem = pd.read_csv("C:/Users/asus/Desktop/classen/fdi_out_vdem.csv", index_col=[0])
Or even better, get rid of the index at the beginning when calling pandas.DataFrame.to_csv:
fdi_out_vdem.to_csv("fdi_out_vdem.csv", index=False)
Streamlit - Applying value_counts / groupby to column selected on run time
I am trying to apply value_counts method to a Dataframe based on the columns selected dynamically in the Streamlit app
This is what I am trying to do:
if st.checkbox("Select Columns To Show"):
all_columns = df.columns.tolist()
selected_columns = st.multiselect("Select", all_columns)
new_df = df[selected_columns]
st.dataframe(new_df)
The above lets me select columns and displays data for the selected columns. I am trying to see how could I apply value_counts/groupby method on this output in Streamlit app
If I try to do the below
st.table(new_df.value_counts())
I get the below error
AttributeError: 'DataFrame' object has no attribute 'value_counts'
I believe the issue lies in passing a list of columns to a dataframe. When you pass a single column in [] to a dataframe, you get back a pandas.Series object (which has the value_counts method). But when you pass a list of columns, you get back a pandas.DataFrame (which doesn't have value_counts method defined on it).
Can you try st.table(new_df[col_name].value_counts()) I think the error is because value_counts() is applicable on a Series and not dataframe.
You can try Converting ".value_counts" output to dataframe
If you want to apply on one single column
def value_counts_df(df, col):
"""
Returns pd.value_counts() as a DataFrame
Parameters
----------
df : Pandas Dataframe
Dataframe on which to run value_counts(), must have column `col`.
col : str
Name of column in `df` for which to generate counts
Returns
-------
Pandas Dataframe
Returned dataframe will have a single column named "count" which contains the count_values()
for each unique value of df[col]. The index name of this dataframe is `col`.
Example
-------
>>> value_counts_df(pd.DataFrame({'a':[1, 1, 2, 2, 2]}), 'a')
count
a
2 3
1 2
"""
df = pd.DataFrame(df[col].value_counts())
df.index.name = col
df.columns = ['count']
return df
val_count_single = value_counts_df(new_df, selected_col)
If you want to apply for all object columns in the dataframe
def valueCountDF(df, object_cols):
c = df[object_cols].apply(lambda x: x.value_counts(dropna=False)).T.stack().astype(int)
p = (df[object_cols].apply(lambda x: x.value_counts(normalize=True,
dropna=False)).T.stack() * 100).round(2)
cp = pd.concat([c,p], axis=1, keys=["Count", "Percentage %"])
return cp
val_count_df_cols = valueCountDF(df, selected_columns)
And Finally, you can use st.table or st.dataframe to show the dataframe in your streamlit app
I got stuck converting a especific dictionary to a pandas dataframe
I have a dictionary and I got stuck while trying to convert to a pandas dataframe.
It's a result of scoring an IBM ML model. The result comes in this format and I would like to transform this dictionary to a pandas dataframe in order to merge later with the original dataframe that was scored.
Dictionary:
{'predictions': [{'fields': ['prediction', 'probability'], 'values': [['Creditworthy', [0.5522992460276774, 0.4477007539723226]]]}]}
image of the code which generated this dictionary
I would like a pandas dataframe like this:
index predictions prediction probability
0 Creditworthy 0.552299 0.447701
Assume that the source dictionary is in value named dct. Start from reading column names: cols = dct['predictions'][0]['fields'] Then create DataFrame in a form which can be read from this dictionary: df = pd.DataFrame(dct['predictions'][0]['values'], columns=['predictions', 'val']) For the time being, values are in val column, as a list: predictions val 0 Creditworthy [0.5522992460276774, 0.4477007539723226] Then break val column into separate columns, setting at the same time proper column names (read before): df[cols] = pd.DataFrame(df.val.values.tolist()) And the only thing to do is to drop val columns: df.drop(columns=['val'], inplace=True) The result is: predictions prediction probability 0 Creditworthy 0.552299 0.447701 Just as it should be.
vectorise pandas: extract multiple dataframes and concat together
I need to extract dataframes from json data stored in every row of initial dataframe and concat them all together. Currently it works for me over iteration and takes ages.
Input data is dataframe, containing JSON dictionaries:
print(json_table)
json_responce timestamp request
27487 {'explore_tabs.. 2019-07-02 02:05:25 Lisboa, Portugal
27488 {'explore_tabs.. 2019-07-02 02:05:27 Ribeira, Portugal
The json_responce field is being unwraped to dataframe:
from pandas.io.json import json_normalize
from ast import literal_eval
json = literal_eval(json_table.loc[0,'json_responce'])
df_normalized = json_normalize(json['explore_tabs'][0]['sections'][0]
['listings'])
which gives a nice unwrapped dataframe for each row of the initial df
Having 27000 rows of json containing df, I iterate over initial df, which creates new df at every step and concat's to the final_df, to concat all the data together:
def unwrap_json_and_concat(json_table):
final_df = pd.DataFrame()
for i in json_table.index:
row = literal_eval(json_table.loc[i,'json_responce'])
df = json_normalize(row['explore_tabs'][0]['sections']
[0]['listings'])
final_df = pd.concat([final_df,df])
return final_df
As expected, that takes ages to iterate over, with significant slowing towards the end of calculation due to the increasing size of the final_df.
I know how to create functions for apply, but I believe it will not give much perfomance either, due to the fact, that new dataframe is being created every row anyways.
How to vectorize this calculation?
Thank you!
Pandas - Appending data from one Dataframe to
I have a Dataframe (called df) that has list of tickets worked for a given date. I have a script that runs each day where this df gets generated and I would like to have a new master dataframe (lets say df_master) that appends values form df to a new Dataframe. So anytime I view df_master I should be able to see all the tickets worked across multiple days. Also would like to have a new column in df_master that shows date when the row was inserted.
Given below is how df looks like:
1001
1002
1003
1004
I tried to perform concat but it threw an error
TypeError: first argument must be an iterable of pandas objects, you passed an object of type "Series"
Update
df_ticket = tickets['ticket']
df_master = df_ticket
df_master['Date'] = pd.Timestamp('now').normalize()
L = [df_master,tickets]
master_df = pd.concat(L)
master_df.to_csv('file.csv', mode='a', header=False, index=False)
I think you need pass sequence to concat, obviously list is used:
objs : a sequence or mapping of Series, DataFrame, or Panel objects
If a dict is passed, the sorted keys will be used as the keys argument, unless it is passed, in which case the values will be selected (see below). Any None objects will be dropped silently unless they are all None in which case a ValueError will be raised
L = [s1,s2]
df = pd.concat(L)
And it seems you pass only Series, so raised error:
df = pd.concat(s)
For insert Date column is possible set pd.Timestamp('now').normalize(), for master df I suggest create one file and append each day DataFrame:
df_ticket = tickets[['ticket']]
df_ticket['Date'] = pd.Timestamp('now').normalize()
df_ticket.to_csv('file.csv', mode='a', header=False, index=False)
df_master = pd.read_csv('file.csv', header=None)