def test_st_legacy_table(self):
"""Test st._legacy_table."""
df = pd.DataFrame([[1, 2], [3, 4]], columns=["col1", "col2"])
st._legacy_table(df)
el = self.get_delta_from_queue().new_element
self.assertEqual(el.table.data.cols[0].int64s.data, [1, 3])
self.assertEqual(el.table.data.cols[1].int64s.data, [2, 4])
self.assertEqual(el.table.columns.plain_index.data.strings.data,
["col1", "col2"])
def _get_unnamed_data_methods(self):
"""DeltaGenerator methods that do not produce named datasets."""
return [
lambda df: st._legacy_dataframe(df),
lambda df: st._legacy_table(df),
lambda df: st._legacy_vega_lite_chart(
df, {"mark": "line", "encoding": {"x": "a", "y": "b"}}
),
# TODO: _legacy_line_chart, _legacy_bar_chart, etc.
]
else: # from .apply(axis=None)
is_max = data == data.max().max()
return pd.DataFrame(np.where(is_max, attr, ""),
index=data.index,
columns=data.columns)
# Create a table to be styled in various ways
np.random.seed(24)
df = pd.DataFrame({"A": np.linspace(1, 5, 5)})
df = pd.concat(
[df, pd.DataFrame(np.random.randn(5, 4), columns=list("BCDE"))], axis=1)
df.iloc[0, 2] = np.nan
# Unstyled
st._legacy_table(df)
# Custom formatting
st._legacy_table(df.style.format("{:.2%}"))
# Colors
st._legacy_table(
df.style.applymap(color_negative_red).apply(highlight_max,
color="darkorange",
axis=0))
# Add rows
x = st._legacy_table(
df.style.set_properties(**{
"background-color": "black",
"color": "lawngreen"
# Copyright 2018-2022 Streamlit Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import streamlit as st import pandas as pd import numpy as np grid = np.arange(0, 100, 1).reshape(10, 10) df = pd.DataFrame(grid) st._legacy_table(df)
st.header("Empty list")
st.write([])
st.header("Empty dataframes")
st.write(np.array(0))
st.write(pd.DataFrame([]))
st._legacy_dataframe()
st._legacy_dataframe([])
st._legacy_dataframe(np.array(0))
st._legacy_dataframe(pd.DataFrame([]))
st.header("Empty one-column dataframes")
st.write(np.array([]))
st._legacy_dataframe(np.array([]))
st.header("Empty two-column dataframes (only shows 1)")
st.write(pd.DataFrame({"lat": [], "lon": []}))
st._legacy_dataframe(pd.DataFrame({"lat": [], "lon": []}))
st.header("Empty tables")
st._legacy_table()
st._legacy_table([])
st._legacy_table(np.array(0))
st._legacy_table(pd.DataFrame([]))
st.header("Empty one-column table")
st._legacy_table(np.array([]))
st.header("Empty two-column table")
st._legacy_table(pd.DataFrame({"lat": [], "lon": []}))
# * 2 line charts (one with only 1 datapoint)
# * 4 vega-lite charts
# * And then all of the above once again.
# (Also, all of the above should have 3 rows or datapoints.)
# * Then all of the above but with no data.
# * Then a 1-row dataframe and an error
num_rows = 3
df = pd.DataFrame({"a": [1, 2, 3], "b": [10, 0, 30], "c": [100, 200, -100]})
df1 = df.iloc[0:1, :]
for test_type in ["coalesce in Py", "coalesce in JS", "clear after addrows"]:
table_el = st._legacy_table(df1)
dataframe_el = st._legacy_dataframe(df1)
chart_el1 = st._legacy_line_chart()
chart_el2 = st._legacy_line_chart(df1)
# 4 identical charts, built in different ways.
vega_el1 = st._legacy_vega_lite_chart(
df1,
{
"mark": {
"type": "line",
"point": True
},
"encoding": {
"x": {
"field": "a",
df = pd.DataFrame(np.random.randn(ROWS, cols),
index=range(ROWS),
columns=range(cols))
st._legacy_dataframe(df)
st.header("Overriding st._legacy_dataframe")
for cols in [4, 5, 6, 20]:
df = pd.DataFrame(np.random.randn(ROWS, cols),
index=range(ROWS),
columns=range(cols))
df_elt = st._legacy_dataframe(np.random.randn(200, 200))
df_elt._legacy_dataframe(df)
st.header("Using st._legacy_table")
for cols in [4, 5, 6, 20]:
df = pd.DataFrame(np.random.randn(ROWS, cols),
index=range(ROWS),
columns=range(cols))
st._legacy_table(df)
st.header("Overriding st._legacy_table")
for cols in [4, 5, 6, 20]:
df = pd.DataFrame(np.random.randn(ROWS, cols),
index=range(ROWS),
columns=range(cols))
df_elt = st._legacy_table(np.random.randn(200, 200))
df_elt._legacy_table(df)
